import numpy as np
import pandas as pd
df1 = pd.read_csv("data/healthcare-dataset-stroke-data.csv")
df2 = pd.read_csv("data/train.csv")
df = pd.concat([df1, df2])
print(df.head())
id gender age hypertension heart_disease ever_married \
0 9046 Male 67.0 0 1 Yes
1 51676 Female 61.0 0 0 Yes
2 31112 Male 80.0 0 1 Yes
3 60182 Female 49.0 0 0 Yes
4 1665 Female 79.0 1 0 Yes
work_type Residence_type avg_glucose_level bmi smoking_status \
0 Private Urban 228.69 36.6 formerly smoked
1 Self-employed Rural 202.21 NaN never smoked
2 Private Rural 105.92 32.5 never smoked
3 Private Urban 171.23 34.4 smokes
4 Self-employed Rural 174.12 24.0 never smoked
stroke
0 1
1 1
2 1
3 1
4 1
# Numerical features - histogram
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
numerical_features = ['age', 'avg_glucose_level', 'bmi']
plt.figure(figsize=(15, 8))
# Plot histograms for numerical features with different colors for stroke=0 and stroke=1
for i, feature in enumerate(numerical_features, 1):
plt.subplot(2, 2, i)
sns.histplot(df[df['stroke'] == 0][feature], bins=30, kde=True, color='blue', label='Stroke = 0')
sns.histplot(df[df['stroke'] == 1][feature], bins=30, kde=True, color='orange', label='Stroke = 1')
plt.title(f'Stroke Distribution for Feature "{feature}"')
plt.legend()
plt.tight_layout()
plt.show()
# Categorical features - bar chart
sns.set(style="whitegrid")
categorical_features = ['gender', 'hypertension', 'heart_disease', 'ever_married', 'work_type', 'Residence_type', 'smoking_status']
plt.figure(figsize=(15, 12))
# Plot count plots for categorical features with different colors for stroke=0 and stroke=1
for i, feature in enumerate(categorical_features, 1):
plt.subplot(3, 3, i)
sns.countplot(x=feature, data=df, hue='stroke', palette=['#5B4FEC',"#FAAE7B"])
plt.title(f'Stroke Distribution for Feature "{feature}"')
plt.tight_layout()
plt.show()
# Numerical features - histogram
sns.set(style="whitegrid")
plt.figure(figsize=(15, 8))
for i, feature in enumerate(numerical_features, 1):
plt.subplot(2, 2, i)
sns.histplot(df[df['stroke'] == 1][feature], bins=30, kde=True)
plt.title(f'Histogram of "{feature}" for Stroke = 1')
plt.tight_layout()
plt.show()
# Categorical features - bar chart
plt.figure(figsize=(15, 12))
for i, feature in enumerate(categorical_features, 1):
plt.subplot(3, 3, i)
sns.countplot(x=feature, data=df[df['stroke'] == 1])
plt.title(f'Bar Chart of "{feature}" for Stroke = 1')
plt.tight_layout()
plt.show()
print("Dataset before processing:")
print("-" * 25)
print(f"Shape: {df.shape}\n")
print(df.astype('object').describe(include='all').loc['unique', :])
print()
print()
print("Dataset after processing:")
print("-" * 25)
df = df[df.smoking_status != "Unknown"]
print(f"Shape after filtering out 'Unknown' values from smoking_status: {df.shape}")
df = df.dropna()
print(f"Shape after dropping NA values: {df.shape}\n")
print(df.astype('object').describe(include='all').loc['unique', :])
Dataset before processing: ------------------------- Shape: (20414, 12) id 19309 gender 3 age 106.0 hypertension 2 heart_disease 2 ever_married 2 work_type 5 Residence_type 2 avg_glucose_level 4644.0 bmi 440.0 smoking_status 4 stroke 2 Name: unique, dtype: object Dataset after processing: ------------------------- Shape after filtering out 'Unknown' values from smoking_status: (14327, 12) Shape after dropping NA values: (14187, 12) id 13712 gender 3 age 82.0 hypertension 2 heart_disease 2 ever_married 2 work_type 5 Residence_type 2 avg_glucose_level 4053.0 bmi 406.0 smoking_status 3 stroke 2 Name: unique, dtype: object
from sklearn.preprocessing import StandardScaler
num_cols = df.columns[df.dtypes.apply(lambda c: np.issubdtype(c, np.number))]
num_cols = num_cols.delete([0, 2, 3, 6])
print(f"numerical data to standardize: {num_cols}\n")
scaler = StandardScaler()
df[num_cols] = scaler.fit_transform(df[num_cols])
print(df.head())
numerical data to standardize: Index(['age', 'avg_glucose_level', 'bmi'], dtype='object')
id gender age hypertension heart_disease ever_married \
0 9046 Male 1.091476 0 1 Yes
2 31112 Male 1.816131 0 1 Yes
3 60182 Female 0.088109 0 0 Yes
4 1665 Female 1.760388 1 0 Yes
5 56669 Male 1.871874 0 0 Yes
work_type Residence_type avg_glucose_level bmi smoking_status \
0 Private Urban 3.922342 1.080068 formerly smoked
2 Private Rural 0.342755 0.443161 never smoked
3 Private Urban 2.246989 0.738313 smokes
4 Self-employed Rural 2.331253 -0.877257 never smoked
5 Private Urban 2.683759 -0.100541 formerly smoked
stroke
0 1
2 1
3 1
4 1
5 1
df = pd.get_dummies(df, columns=['gender', 'work_type', 'Residence_type', 'smoking_status'], dtype=int)
df.replace({"Yes": 1, "No": 0}, inplace=True)
print(df.head())
id age hypertension heart_disease ever_married \ 0 9046 1.091476 0 1 1 2 31112 1.816131 0 1 1 3 60182 0.088109 0 0 1 4 1665 1.760388 1 0 1 5 56669 1.871874 0 0 1 avg_glucose_level bmi stroke gender_Female gender_Male ... \ 0 3.922342 1.080068 1 0 1 ... 2 0.342755 0.443161 1 0 1 ... 3 2.246989 0.738313 1 1 0 ... 4 2.331253 -0.877257 1 1 0 ... 5 2.683759 -0.100541 1 0 1 ... work_type_Govt_job work_type_Never_worked work_type_Private \ 0 0 0 1 2 0 0 1 3 0 0 1 4 0 0 0 5 0 0 1 work_type_Self-employed work_type_children Residence_type_Rural \ 0 0 0 0 2 0 0 1 3 0 0 0 4 1 0 1 5 0 0 0 Residence_type_Urban smoking_status_formerly smoked \ 0 1 1 2 0 0 3 1 0 4 0 0 5 1 1 smoking_status_never smoked smoking_status_smokes 0 0 0 2 1 0 3 0 1 4 1 0 5 0 0 [5 rows x 21 columns]
correlation_matrix = df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=False, cmap='coolwarm', fmt=".2f", linewidths=0.5)
plt.title('Correlation Heatmap')
plt.show()
correlation_with_target = correlation_matrix['stroke'].sort_values(ascending=False)
plt.figure(figsize=(15, 6))
sns.barplot(x=correlation_with_target.index, y=correlation_with_target.values, palette='viridis')
plt.xticks(rotation=45, ha='right', rotation_mode='anchor')
plt.title('Feature Correlation with Stroke Likelihood')
plt.show()
There's a heavy class imbalance in the dataset favoring the majority class, stroke = 0.
stroke_counts = df['stroke'].value_counts()
# Create a bar plot
plt.bar(stroke_counts.index, stroke_counts.values, color=['red', 'blue'])
# Add labels and title
plt.xlabel('Stroke')
plt.ylabel('Count')
plt.xticks([0, 1])
plt.title('Bar Plot of Stroke Values')
# Show the plot
plt.show()
from sklearn.model_selection import train_test_split
from sklearn.utils import class_weight
import time
weights = class_weight.compute_class_weight('balanced', classes=[0, 1], y=df['stroke'])
w = [weights[0] if stroke == 0 else weights[1] for stroke in df['stroke']]
y = df.pop('stroke')
X = df.to_numpy()
def train_test_weight_data():
seed = np.random.seed(0)
return train_test_split(X, y, w, test_size=0.2, random_state=seed)
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
print(len(X_tr), len(X_te), len(y_tr), len(y_te), len(w_tr), len(w_te))
11349 2838 11349 2838 11349 2838
from imblearn.over_sampling import SMOTE
def smote_sample(X_tr, y_tr):
sm = SMOTE(random_state=0)
# Synethetic sampling - balancing the minority class with the majority class by generating extra synthetic samples for training
X_res, y_res = sm.fit_resample(X_tr, y_tr)
return X_res, y_res
X_res, y_res = smote_sample(X_tr, y_tr)
print(f"SMOTE synethetically sampled data - X.shape: {X_res.shape}, y.shape: {y_res.shape}")
SMOTE synethetically sampled data - X.shape: (21592, 20), y.shape: (21592,)
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, classification_report, confusion_matrix, auc, precision_recall_curve
def weight_accuracy(predicted_y, true_y, weight):
sum_weighted_correct_predictions = 0
for yhat, y, w in zip(predicted_y, true_y, weight):
if yhat == y:
sum_weighted_correct_predictions += (1 * w)
total_weight = sum(weight)
return sum_weighted_correct_predictions / total_weight
def eval_model(predicted_y, true_y, weight):
# Calculate precision, recall, and f1 score (useful for problems w/ class imbalances)
precision = precision_score(true_y, predicted_y, sample_weight=weight)
recall = recall_score(true_y, predicted_y, sample_weight=weight)
f1 = f1_score(true_y, predicted_y, sample_weight=weight)
weighted_accuracy = weight_accuracy(predicted_y, true_y, weight)
# Print metrics
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
print("Weighted accuracy:", weighted_accuracy)
print("-" * 20)
# Print classification report and confusion matrix
print("Classification Report:\n", classification_report(true_y, predicted_y, sample_weight=weight))
print("\nConfusion Matrix:\n", confusion_matrix(true_y, predicted_y, sample_weight=weight))
# Graph PR-curve
p, r, _ = precision_recall_curve(true_y, predicted_y, sample_weight=weight)
pr_auc = auc(r, p)
plt.figure(figsize=(8, 8))
plt.plot(r, p, color='darkorange', lw=2, label='PR curve (area = {:.2f})'.format(pr_auc))
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.title('Precision-Recall Curve')
plt.legend(loc='lower right')
plt.show()
from sklearn.model_selection import GridSearchCV
from sklearn.base import ClassifierMixin
def do_grid_search(classifier: ClassifierMixin, param_grid: dict, n_jobs=4) -> GridSearchCV:
"""
Perform a GridSearchCV using the provided classifier model and parameter grid.
"""
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
X_res, y_res = smote_sample(X_tr, y_tr)
grid_search = GridSearchCV(estimator=classifier, param_grid=param_grid, cv=5, scoring='accuracy', verbose=5, n_jobs=n_jobs)
grid_search.fit(X_res, y_res)
"""
Evaluate the best model found by the GridSearchCV.
"""
best_params = grid_search.best_params_
print(f"Best parameters: {best_params}")
print()
best_model = grid_search.best_estimator_
y_pred_te = best_model.predict(X_te)
eval_model(y_pred_te, y_te, w_te)
return grid_search
def plot_gridsearch_2d_hyperparameter_accuracy_heatmap(grid_search: GridSearchCV, param1_name: str, param2_name: str) -> None:
"""
Plot a 2D heatmap showing the GridSearchCV's accuracy across the two hyperparameters' searched values.
"""
df = pd.DataFrame(grid_search.cv_results_)
heatmap_data = df.pivot_table(index=f"param_{param1_name}", columns=f"param_{param2_name}", values='mean_test_score')
# Create heatmap of the mean test scores given certain hyperparameter configurations
plt.figure(figsize=(12, 8))
sns.heatmap(heatmap_data, annot=True, cmap='vlag', fmt='.4f', cbar_kws={'label': 'Accuracy Score'})
plt.title('Grid Search Results')
plt.xlabel(param2_name)
plt.ylabel(param1_name)
plt.yticks(rotation=0)
plt.show()
def do_grid_search_full_task(classifier: ClassifierMixin, param_grid: dict, param1_name: str=None, param2_name: str=None, n_jobs=4) -> GridSearchCV:
"""
Performs grid search, evaluates the best model, and plots the heatmap of the two given params.
returns: GridSearchCV
"""
grid_search = do_grid_search(classifier, param_grid, n_jobs=n_jobs)
print("\n" + "-" * 20)
if param1_name and param2_name:
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(grid_search, param1_name, param2_name)
return grid_search
from sklearn.linear_model import LogisticRegression
def do_logistic_regression(X_tr, y_tr, X_te, y_te, w_tr, w_te):
logistic_regression_model = LogisticRegression()
# Train the model
logistic_regression_model.fit(X_tr, y_tr)
# Training weighted accuracy
y_pred_tr = logistic_regression_model.predict(X_tr)
if w_tr:
print(f"Training accuracy: {weight_accuracy(y_pred_tr, y_tr, w_tr)}")
else:
print(f"Training score: {logistic_regression_model.score(X_tr, y_tr)}")
# Validation weighted accuracy
y_pred_te = logistic_regression_model.predict(X_te)
print(f"Validation accuracy: {weight_accuracy(y_pred_te, y_te, w_te)}")
print(f"Score: {logistic_regression_model.score(X_te, y_te, sample_weight=w_te)}\n")
eval_model(y_pred_te, y_te, w_te)
Very low recall / precision for class 1 means the classifier is predicting class 0 (the majority class) most of the time, which isn't ideal.
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
do_logistic_regression(X_tr, y_tr, X_te, y_te, w_tr, w_te)
Training accuracy: 0.509837124645882
Validation accuracy: 0.4812733333825796
Score: 0.481273333382558
Precision: 0.0
Recall: 0.0
F1 Score: 0.0
Weighted accuracy: 0.4812733333825796
--------------------
Classification Report:
precision recall f1-score support
0 0.48 1.00 0.65 1413.6493732849112
1 0.00 0.00 0.00 1521.4751420454538
accuracy 0.48 2935.1245153303653
macro avg 0.24 0.50 0.32 2935.1245153303653
weighted avg 0.23 0.48 0.31 2935.1245153303653
Confusion Matrix:
[[1.41259716e+03 1.05221390e+00]
[1.52147514e+03 0.00000000e+00]]
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
Improved precision, recall, and weighted accuracy.
# Fitting the model using the synthetic samples
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
X_res, y_res = smote_sample(X_tr, y_tr)
do_logistic_regression(X_res, y_res, X_te, y_te, None, w_te)
Training score: 0.793442015561319
Validation accuracy: 0.7683851714325161
Score: 0.7683851714324925
Precision: 0.7775682369902973
Recall: 0.774834437086094
F1 Score: 0.7761989299108307
Weighted accuracy: 0.7683851714325161
--------------------
Classification Report:
precision recall f1-score support
0 0.76 0.76 0.76 1413.6493732849112
1 0.78 0.77 0.78 1521.4751420454538
accuracy 0.77 2935.1245153303653
macro avg 0.77 0.77 0.77 2935.1245153303653
weighted avg 0.77 0.77 0.77 2935.1245153303653
Confusion Matrix:
[[1076.41481866 337.23455462]
[ 342.58380682 1178.89133523]]
log_reg_model = LogisticRegression()
log_reg_param_grid = {
'solver': ['newton-cg', 'lbfgs', 'liblinear'],
'penalty': ['l2'],
'C': [100, 10, 1.0, 0.1, 0.01]
}
log_reg_grid_search = do_grid_search_full_task(log_reg_model, log_reg_param_grid, 'solver', 'C')
Fitting 5 folds for each of 15 candidates, totalling 75 fits
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
[CV 1/5] END C=100, penalty=l2, solver=newton-cg;, score=0.789 total time= 2.4s [CV 2/5] END C=100, penalty=l2, solver=newton-cg;, score=0.803 total time= 2.5s [CV 4/5] END C=100, penalty=l2, solver=newton-cg;, score=0.801 total time= 2.5s [CV 3/5] END C=100, penalty=l2, solver=newton-cg;, score=0.791 total time= 2.5s [CV 3/5] END ...C=100, penalty=l2, solver=lbfgs;, score=0.500 total time= 0.0s [CV 1/5] END ...C=100, penalty=l2, solver=lbfgs;, score=0.785 total time= 0.1s [CV 4/5] END ...C=100, penalty=l2, solver=lbfgs;, score=0.797 total time= 0.1s [CV 2/5] END ...C=100, penalty=l2, solver=lbfgs;, score=0.800 total time= 0.2s [CV 1/5] END C=100, penalty=l2, solver=liblinear;, score=0.789 total time= 0.0s [CV 2/5] END C=100, penalty=l2, solver=liblinear;, score=0.803 total time= 0.0s [CV 3/5] END C=100, penalty=l2, solver=liblinear;, score=0.793 total time= 0.0s [CV 4/5] END C=100, penalty=l2, solver=liblinear;, score=0.800 total time= 0.0s [CV 5/5] END ...C=100, penalty=l2, solver=lbfgs;, score=0.797 total time= 0.1s [CV 5/5] END C=100, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
[CV 5/5] END C=100, penalty=l2, solver=newton-cg;, score=0.801 total time= 2.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations. warnings.warn(
[CV 2/5] END C=10, penalty=l2, solver=newton-cg;, score=0.803 total time= 2.1s [CV 1/5] END C=10, penalty=l2, solver=newton-cg;, score=0.789 total time= 2.2s [CV 3/5] END C=10, penalty=l2, solver=newton-cg;, score=0.791 total time= 2.3s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
[CV 1/5] END ....C=10, penalty=l2, solver=lbfgs;, score=0.785 total time= 0.2s [CV 3/5] END ....C=10, penalty=l2, solver=lbfgs;, score=0.500 total time= 0.0s [CV 2/5] END ....C=10, penalty=l2, solver=lbfgs;, score=0.796 total time= 0.1s [CV 4/5] END ....C=10, penalty=l2, solver=lbfgs;, score=0.797 total time= 0.2s [CV 5/5] END ....C=10, penalty=l2, solver=lbfgs;, score=0.798 total time= 0.2s [CV 2/5] END C=10, penalty=l2, solver=liblinear;, score=0.803 total time= 0.0s [CV 1/5] END C=10, penalty=l2, solver=liblinear;, score=0.789 total time= 0.1s [CV 3/5] END C=10, penalty=l2, solver=liblinear;, score=0.791 total time= 0.0s [CV 4/5] END C=10, penalty=l2, solver=liblinear;, score=0.800 total time= 0.0s [CV 5/5] END C=10, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
[CV 4/5] END C=10, penalty=l2, solver=newton-cg;, score=0.801 total time= 2.2s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
[CV 5/5] END C=10, penalty=l2, solver=newton-cg;, score=0.800 total time= 2.4s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
[CV 1/5] END C=1.0, penalty=l2, solver=newton-cg;, score=0.789 total time= 2.2s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:210: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.
warnings.warn(
[CV 2/5] END C=1.0, penalty=l2, solver=newton-cg;, score=0.803 total time= 3.1s [CV 1/5] END ...C=1.0, penalty=l2, solver=lbfgs;, score=0.786 total time= 0.2s [CV 2/5] END ...C=1.0, penalty=l2, solver=lbfgs;, score=0.796 total time= 0.2s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
[CV 3/5] END ...C=1.0, penalty=l2, solver=lbfgs;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
[CV 3/5] END C=1.0, penalty=l2, solver=newton-cg;, score=0.791 total time= 2.8s [CV 4/5] END ...C=1.0, penalty=l2, solver=lbfgs;, score=0.796 total time= 0.2s [CV 1/5] END C=1.0, penalty=l2, solver=liblinear;, score=0.789 total time= 0.0s [CV 5/5] END ...C=1.0, penalty=l2, solver=lbfgs;, score=0.764 total time= 0.1s [CV 3/5] END C=1.0, penalty=l2, solver=liblinear;, score=0.791 total time= 0.0s [CV 2/5] END C=1.0, penalty=l2, solver=liblinear;, score=0.803 total time= 0.0s [CV 5/5] END C=1.0, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s [CV 4/5] END C=1.0, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
[CV 4/5] END C=1.0, penalty=l2, solver=newton-cg;, score=0.800 total time= 2.6s [CV 5/5] END C=1.0, penalty=l2, solver=newton-cg;, score=0.800 total time= 2.3s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
[CV 2/5] END C=0.1, penalty=l2, solver=newton-cg;, score=0.803 total time= 1.3s [CV 1/5] END C=0.1, penalty=l2, solver=newton-cg;, score=0.789 total time= 1.3s [CV 1/5] END ...C=0.1, penalty=l2, solver=lbfgs;, score=0.785 total time= 0.2s [CV 3/5] END C=0.1, penalty=l2, solver=newton-cg;, score=0.792 total time= 1.2s [CV 3/5] END ...C=0.1, penalty=l2, solver=lbfgs;, score=0.500 total time= 0.0s [CV 4/5] END C=0.1, penalty=l2, solver=newton-cg;, score=0.801 total time= 1.2s [CV 2/5] END ...C=0.1, penalty=l2, solver=lbfgs;, score=0.796 total time= 0.2s [CV 5/5] END ...C=0.1, penalty=l2, solver=lbfgs;, score=0.764 total time= 0.0s [CV 4/5] END ...C=0.1, penalty=l2, solver=lbfgs;, score=0.787 total time= 0.1s [CV 2/5] END C=0.1, penalty=l2, solver=liblinear;, score=0.804 total time= 0.0s [CV 1/5] END C=0.1, penalty=l2, solver=liblinear;, score=0.788 total time= 0.0s [CV 3/5] END C=0.1, penalty=l2, solver=liblinear;, score=0.793 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:416: LineSearchWarning: Rounding errors prevent the line search from converging
warn(msg, LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/utils/optimize.py:203: UserWarning: Line Search failed
warnings.warn("Line Search failed")
[CV 4/5] END C=0.1, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s [CV 5/5] END C=0.1, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
[CV 1/5] END C=0.01, penalty=l2, solver=newton-cg;, score=0.788 total time= 0.7s [CV 2/5] END C=0.01, penalty=l2, solver=newton-cg;, score=0.802 total time= 0.7s [CV 5/5] END C=0.1, penalty=l2, solver=newton-cg;, score=0.800 total time= 1.4s [CV 3/5] END C=0.01, penalty=l2, solver=newton-cg;, score=0.792 total time= 0.9s [CV 1/5] END ..C=0.01, penalty=l2, solver=lbfgs;, score=0.779 total time= 0.1s [CV 3/5] END ..C=0.01, penalty=l2, solver=lbfgs;, score=0.500 total time= 0.0s [CV 2/5] END ..C=0.01, penalty=l2, solver=lbfgs;, score=0.777 total time= 0.1s [CV 4/5] END ..C=0.01, penalty=l2, solver=lbfgs;, score=0.777 total time= 0.1s [CV 5/5] END ..C=0.01, penalty=l2, solver=lbfgs;, score=0.764 total time= 0.1s [CV 1/5] END C=0.01, penalty=l2, solver=liblinear;, score=0.788 total time= 0.0s [CV 2/5] END C=0.01, penalty=l2, solver=liblinear;, score=0.802 total time= 0.0s [CV 3/5] END C=0.01, penalty=l2, solver=liblinear;, score=0.792 total time= 0.0s [CV 4/5] END C=0.01, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s [CV 5/5] END C=0.01, penalty=l2, solver=liblinear;, score=0.801 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:457: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/scipy/optimize/_linesearch.py:306: LineSearchWarning: The line search algorithm did not converge
warn('The line search algorithm did not converge', LineSearchWarning)
[CV 4/5] END C=0.01, penalty=l2, solver=newton-cg;, score=0.799 total time= 0.7s
[CV 5/5] END C=0.01, penalty=l2, solver=newton-cg;, score=0.801 total time= 0.7s
Best parameters: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}
Precision: 0.7870037084453668
Recall: 0.7947019867549681
F1 Score: 0.7908341135559289
Weighted accuracy: 0.7820895355181944
--------------------
Classification Report:
precision recall f1-score support
0 0.78 0.77 0.77 1413.6493732849112
1 0.79 0.79 0.79 1521.4751420454538
accuracy 0.78 2935.1245153303653
macro avg 0.78 0.78 0.78 2935.1245153303653
weighted avg 0.78 0.78 0.78 2935.1245153303653
Confusion Matrix:
[[1086.4108507 327.23852258]
[ 312.35582386 1209.11931818]]
--------------------
import joblib
joblib.dump(log_reg_grid_search.best_estimator_, 'models/log_reg_best_model.joblib')
['models/log_regs_best_model.joblib']
# Log-reg model's coefficients
coefficients = log_reg_grid_search.best_estimator_.coef_
print(coefficients)
print(df.columns)
coefficients_df = pd.DataFrame(coefficients, columns=df.columns)
# Plotting the heatmap
plt.figure(figsize=(12, 1))
sns.heatmap(coefficients_df, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5, yticklabels=0)
plt.title('Logistic Regression Coefficients - Correlation w/ Stroke Heatmap')
plt.show()
[[-6.45303003e-06 1.87329014e+00 5.51776393e-01 6.32308552e-01
5.53089560e-02 2.23989698e-01 1.38037262e-01 -3.21162374e-01
-1.84859108e-01 -5.78116561e-04 -8.22414778e-02 -8.45157254e-03
-6.30746812e-02 -3.16018424e-01 -3.68134436e-02 -2.26936900e-01
-2.79662699e-01 -3.68597507e-01 -3.49801490e-01 2.11799397e-01]]
Index(['id', 'age', 'hypertension', 'heart_disease', 'ever_married',
'avg_glucose_level', 'bmi', 'gender_Female', 'gender_Male',
'gender_Other', 'work_type_Govt_job', 'work_type_Never_worked',
'work_type_Private', 'work_type_Self-employed', 'work_type_children',
'Residence_type_Rural', 'Residence_type_Urban',
'smoking_status_formerly smoked', 'smoking_status_never smoked',
'smoking_status_smokes'],
dtype='object')
from sklearn.tree import DecisionTreeClassifier
def do_decision_tree_classification(X_tr, y_tr, X_te, y_te, w_tr, w_te):
dt_model = DecisionTreeClassifier()
# Train the model
dt_model.fit(X_tr, y_tr)
# Training weighted accuracy
y_pred_tr = dt_model.predict(X_tr)
if w_tr:
print(f"Training accuracy: {weight_accuracy(y_pred_tr, y_tr, w_tr)}")
else:
print(f"Training score: {dt_model.score(X_tr, y_tr)}")
# Validation weighted accuracy
y_pred_te = dt_model.predict(X_te)
print(f"Validation accuracy: {weight_accuracy(y_pred_te, y_te, w_te)}")
print(f"Score: {dt_model.score(X_te, y_te, sample_weight=w_te)}\n")
eval_model(y_pred_te, y_te, w_te)
# Fitting the model using the synthetic samples
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
X_res, y_res = smote_sample(X_tr, y_tr)
do_decision_tree_classification(X_res, y_res, X_te, y_te, None, w_te)
Training score: 1.0
Validation accuracy: 0.5608065116925983
Score: 0.5608065116925661
Precision: 0.7961678613130739
Recall: 0.20529801324503313
F1 Score: 0.3264248623733556
Weighted accuracy: 0.5608065116925983
--------------------
Classification Report:
precision recall f1-score support
0 0.52 0.94 0.67 1413.6493732849112
1 0.80 0.21 0.33 1521.4751420454538
accuracy 0.56 2935.1245153303653
macro avg 0.66 0.57 0.50 2935.1245153303653
weighted avg 0.67 0.56 0.49 2935.1245153303653
Confusion Matrix:
[[1333.68111696 79.96825632]
[1209.11931818 312.35582386]]
from sklearn.tree import DecisionTreeClassifier
dt_model = DecisionTreeClassifier()
dt_param_grid = {
'max_depth': [None, 10, 20, 30, 50, 70, 100],
'max_features': ['auto', 'sqrt', 'log2'],
'max_leaf_nodes': [None, 10, 20, 30, 50, 70, 100],
'min_impurity_decrease': [0.0, 0.1, 0.2]
}
dt_grid_search = do_grid_search_full_task(dt_model, dt_param_grid)
Fitting 5 folds for each of 441 candidates, totalling 2205 fits [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.748 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.879 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.843 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.821 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.906 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.783 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.880 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.862 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.908 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.874 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.916 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.900 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.849 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.914 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.924 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s[CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.941 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.839 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.849 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s [CV 2/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.949 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 3/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.958 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.765 total time= 0.0s [CV 5/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.845 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.877 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.845 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.908 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.877 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.963 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.823 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.924 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.910 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.894 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.891 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.821 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.834 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.830 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.850 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.921 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.917 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.921 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s[CV 1/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.847 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.949 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.875 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.787 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.842 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.874 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.838 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.967 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.817 total time= 0.0s [CV 5/5] END max_depth=None, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.968 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.896 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.864 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.816 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.870 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.910 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.908 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.919 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.842 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.903 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.922 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.854 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.951 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.853 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.948 total time= 0.0s [CV 3/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.791 total time= 0.0s [CV 4/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.813 total time= 0.0s [CV 2/5] END max_depth=None, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.816 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.859 total time= 0.0s[CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.855 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.884 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.825 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.865 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.819 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.812 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.911 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.900 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.907 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.943 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.919 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.921 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.845 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.843 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 1/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.851 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.854 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.951 total time= 0.0s [CV 3/5] END max_depth=10, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.927 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.932 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.768 total time= 0.0s[CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.834 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.837 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.804 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.861 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.874 total time= 0.1s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.897 total time= 0.1s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.834 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.835 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.890 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.866 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.910 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.869 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.891 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.922 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.826 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.845 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.949 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.791 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.856 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.830 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.929 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.838 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.778 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 1/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.811 total time= 0.0s [CV 3/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.812 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 5/5] END max_depth=10, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.892 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.874 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.854 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.806 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.905 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.868 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.829 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.835 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.896 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.907 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.890 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.932 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.956 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.935 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 3/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.837 total time= 0.0s [CV 5/5] END max_depth=10, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.830 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.963 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.865 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.899 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.811 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.862 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.812 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.885 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.891 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.844 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.935 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.897 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.888 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.928 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.929 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.848 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.922 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.836 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.804 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.932 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.952 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.970 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.839 total time= 0.0s [CV 5/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.857 total time= 0.0s [CV 1/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.968 total time= 0.0s [CV 2/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.833 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.827 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.884 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.828 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.905 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.879 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.881 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.839 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.903 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.851 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.846 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.932 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.899 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.922 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.942 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.852 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.919 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.943 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.941 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.841 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.806 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.846 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.941 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.880 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.869 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.867 total time= 0.0s [CV 1/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 5/5] END max_depth=20, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.963 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.802 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.898 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.879 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.900 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.800 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.899 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.837 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.903 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.858 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.886 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.833 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.922 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.874 total time= 0.0s [CV 2/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.929 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.948 total time= 0.0s [CV 3/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.928 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.963 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.969 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.955 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=20, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.941 total time= 0.0s [CV 1/5] END max_depth=20, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.795 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.846 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.877 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.829 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.833 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.848 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.906 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.887 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.833 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.919 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.927 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.911 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.843 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.942 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.897 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.927 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.851 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.952 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.862 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.816 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.830 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.905 total time= 0.0s [CV 1/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.911 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.866 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.888 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.924 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.905 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.899 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.820 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.943 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.835 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.839 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.854 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s [CV 1/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.968 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.782 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.857 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.804 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.969 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.820 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.828 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.907 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.806 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.879 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.906 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.896 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.917 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.890 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.904 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.846 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.848 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.922 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.899 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.875 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.843 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.941 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.943 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.957 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.809 total time= 0.0s [CV 3/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.847 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=30, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.828 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.817 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.826 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.868 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.818 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.908 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.841 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.884 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.903 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.944 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.881 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.881 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.948 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.907 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.953 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.926 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.847 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.969 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.823 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.849 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.874 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.791 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.967 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.856 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.860 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.819 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.886 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.839 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.858 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.850 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.910 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.906 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.853 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.891 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.896 total time= 0.0s[CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.935 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.862 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.916 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.807 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.817 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.860 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.814 total time= 0.0s [CV 5/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.958 total time= 0.0s [CV 1/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.853 total time= 0.0s [CV 2/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.963 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 3/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.838 total time= 0.0s [CV 4/5] END max_depth=50, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.896 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.890 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.842 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.841 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.910 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.836 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.914 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.941 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.897 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.929 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.935 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.849 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.913 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.927 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.916 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.953 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.924 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 3/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.866 total time= 0.0s [CV 4/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 5/5] END max_depth=50, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.831 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.893 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.879 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.905 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.901 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.802 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.770 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.903 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.908 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.834 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.881 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.916 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.843 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.916 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.855 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.850 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.958 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.948 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.957 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.919 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.957 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.951 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.778 total time= 0.0s [CV 1/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.968 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.821 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.860 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.794 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.829 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.822 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.826 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.852 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.864 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.913 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.867 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.890 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.848 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.884 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.934 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.939 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.858 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.872 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.967 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.940 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.810 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.963 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.874 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.954 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.805 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.834 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.877 total time= 0.0s [CV 5/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 1/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.823 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.872 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.889 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.914 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.908 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.796 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.809 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.933 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.878 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.840 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.837 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.926 total time= 0.0s [CV 1/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.846 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.919 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.952 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.871 total time= 0.0s [CV 2/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.912 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s [CV 3/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.953 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.967 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.956 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.927 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.966 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=70, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.936 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.774 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.864 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.962 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.793 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.862 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.809 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.786 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.833 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.875 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.894 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.869 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.893 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.838 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.911 total time= 0.0s [CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.855 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.863 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.847 total time= 0.0s [CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s [CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.956 total time= 0.0s [CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn( /Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.932 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.928 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.918 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.856 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.961 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.877 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.959 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.967 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.946 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.779 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.953 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.822 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.792 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.964 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.823 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.868 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=auto, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.856 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.956 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.828 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.873 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.882 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.820 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.842 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.884 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.916 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.886 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.923 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.882 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.904 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.929 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.938 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.920 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.928 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.799 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.832 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.852 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.848 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.876 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.947 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.956 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=sqrt, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.942 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.958 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.965 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.781 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.842 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=None, min_impurity_decrease=0.0;, score=0.960 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.853 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.816 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.846 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.816 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=10, min_impurity_decrease=0.0;, score=0.815 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.909 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.877 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.937 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.895 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.825 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.0;, score=0.894 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.880 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=30, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.917 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.0;, score=0.884 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.931 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=20, min_impurity_decrease=0.1;, score=0.787 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.915 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.836 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.945 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.928 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.0;, score=0.925 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.0;, score=0.930 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.803 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.1;, score=0.795 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=50, min_impurity_decrease=0.1;, score=0.785 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=70, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.958 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.861 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.942 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.932 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.1;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.0;, score=0.950 total time= 0.0s
[CV 2/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 1/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 3/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 4/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
[CV 5/5] END max_depth=100, max_features=log2, max_leaf_nodes=100, min_impurity_decrease=0.2;, score=0.500 total time= 0.0s
Best parameters: {'max_depth': 70, 'max_features': 'auto', 'max_leaf_nodes': None, 'min_impurity_decrease': 0.0}
Precision: 0.8291134938846562
Recall: 0.25165562913907286
F1 Score: 0.3861159122449447
Weighted accuracy: 0.5851953148770529
--------------------
Classification Report:
precision recall f1-score support
0 0.54 0.94 0.69 1413.6493732849112
1 0.83 0.25 0.39 1521.4751420454538
accuracy 0.59 2935.1245153303653
macro avg 0.68 0.60 0.54 2935.1245153303653
weighted avg 0.69 0.59 0.53 2935.1245153303653
Confusion Matrix:
[[1334.73333086 78.91604242]
[1138.58735795 382.88778409]]
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`. warnings.warn(
--------------------
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(dt_grid_search, "max_depth", "max_features")
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(dt_grid_search, "max_depth", "max_leaf_nodes")
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(dt_grid_search, "max_depth", "min_impurity_decrease")
import joblib
joblib.dump(dt_grid_search.best_estimator_, 'models/dt_best_model.joblib')
['dt_best_model.joblib']
from sklearn.ensemble import RandomForestClassifier
def do_random_forest_classification(X_tr, y_tr, X_te, y_te, w_tr, w_te):
rf_model = RandomForestClassifier()
# Train the model
rf_model.fit(X_tr, y_tr)
# Training weighted accuracy
y_pred_tr = rf_model.predict(X_tr)
if w_tr:
print(f"Training accuracy: {weight_accuracy(y_pred_tr, y_tr, w_tr)}")
else:
print(f"Training score: {rf_model.score(X_tr, y_tr)}")
# Validation weighted accuracy
y_pred_te = rf_model.predict(X_te)
print(f"Validation accuracy: {weight_accuracy(y_pred_te, y_te, w_te)}")
print(f"Score: {rf_model.score(X_te, y_te, sample_weight=w_te)}\n")
eval_model(y_pred_te, y_te, w_te)
# Fitting the model using the synthetic samples
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
X_res, y_res = smote_sample(X_tr, y_tr)
do_random_forest_classification(X_res, y_res, X_te, y_te, None, w_te)
Training score: 1.0
Validation accuracy: 0.48870411584130036
Score: 0.4887041158412775
Precision: 0.7614502739029759
Recall: 0.01986754966887418
F1 Score: 0.038724705057887464
Weighted accuracy: 0.48870411584130036
--------------------
Classification Report:
precision recall f1-score support
0 0.48 0.99 0.65 1413.6493732849112
1 0.76 0.02 0.04 1521.4751420454538
accuracy 0.49 2935.1245153303653
macro avg 0.62 0.51 0.35 2935.1245153303653
weighted avg 0.63 0.49 0.33 2935.1245153303653
Confusion Matrix:
[[1404.17944819 9.46992509]
[1491.24715909 30.22798295]]
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier()
rf_param_grid = {
'n_estimators': [100, 200, 400, 800],
'max_depth': [None, 2, 4, 8, 16, 32, 64, 128],
'min_samples_split': [2, 5, 10],
'min_samples_leaf': [1, 2, 4],
'bootstrap': [True, False]
}
rf_grid_search = do_grid_search_full_task(rf_model, rf_param_grid, n_jobs=7)
Fitting 5 folds for each of 576 candidates, totalling 2880 fits
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.876 total time= 1.7s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.9s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.0s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.876 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.5s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.4s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.876 total time= 6.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.8s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.6s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.8s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.874 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.9s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.9s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.877 total time= 12.8s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.874 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.996 total time= 13.8s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.1s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 14.1s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.1s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.1s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.874 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.1s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.1s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 7.2s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.2s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.873 total time= 1.7s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.0s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.875 total time= 13.3s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.873 total time= 3.3s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.5s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.1s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.1s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.1s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.873 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.8s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 7.1s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.992 total time= 7.1s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 7.0s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.873 total time= 1.8s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.0s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.0s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.8s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.873 total time= 13.4s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.874 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 14.2s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 14.6s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.6s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.993 total time= 14.2s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 14.2s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.873 total time= 6.0s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.5s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.873 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.996 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.872 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.873 total time= 12.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 12.8s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.1s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.0s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.1s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 12.8s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.0s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.873 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.6s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.996 total time= 6.7s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.873 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.871 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.873 total time= 12.5s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.5s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.3s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.4s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.5s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.872 total time= 6.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.996 total time= 6.7s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.8s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.992 total time= 6.7s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.870 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.871 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.872 total time= 12.7s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 13.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.992 total time= 3.5s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 14.0s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.993 total time= 13.9s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.869 total time= 6.5s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.7s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.4s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.870 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.8s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.992 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.869 total time= 3.3s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.871 total time= 13.0s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.5s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.6s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 13.5s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.992 total time= 3.5s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 13.5s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 13.2s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.870 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.3s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.3s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.6s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.870 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.870 total time= 12.7s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.870 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.5s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.7s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.4s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.6s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.869 total time= 6.2s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.7s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.5s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.816 total time= 0.7s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.6s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.881 total time= 0.6s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.877 total time= 0.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.865 total time= 0.6s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.879 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.821 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.870 total time= 1.4s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.858 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.860 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.848 total time= 1.3s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.816 total time= 2.6s
[CV 1/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.870 total time= 13.0s
[CV 2/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 13.2s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.870 total time= 2.5s
[CV 3/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.8s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.855 total time= 2.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.863 total time= 2.5s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.860 total time= 2.5s
[CV 4/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.992 total time= 12.8s
[CV 5/5] END bootstrap=True, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.814 total time= 0.7s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.859 total time= 0.6s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.852 total time= 0.7s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.820 total time= 5.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.864 total time= 0.8s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.856 total time= 0.7s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.820 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.878 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.860 total time= 5.2s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.846 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.847 total time= 5.5s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.867 total time= 5.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.845 total time= 5.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.871 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.865 total time= 1.3s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.825 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.844 total time= 2.5s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.861 total time= 2.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.855 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.854 total time= 2.7s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.801 total time= 0.7s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.862 total time= 0.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.846 total time= 0.6s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.843 total time= 0.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.871 total time= 0.7s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.814 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.816 total time= 5.2s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.874 total time= 5.3s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.872 total time= 1.2s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.836 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.853 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.860 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.867 total time= 5.0s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.847 total time= 5.2s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.867 total time= 5.2s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.819 total time= 2.4s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.854 total time= 2.5s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.849 total time= 2.5s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.852 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.863 total time= 2.7s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.819 total time= 0.7s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.862 total time= 0.6s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.840 total time= 0.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.871 total time= 0.6s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.859 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.820 total time= 5.1s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.820 total time= 1.2s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.860 total time= 5.2s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.878 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.850 total time= 5.1s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.848 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.859 total time= 1.2s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.848 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.856 total time= 5.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.849 total time= 5.1s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.813 total time= 2.5s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.866 total time= 2.5s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.861 total time= 2.5s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.854 total time= 2.5s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.846 total time= 2.5s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.868 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.818 total time= 0.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.849 total time= 0.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.866 total time= 0.6s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.841 total time= 0.8s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.821 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.873 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.871 total time= 5.1s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.815 total time= 5.4s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.859 total time= 1.4s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.847 total time= 5.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.877 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.841 total time= 1.4s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.863 total time= 5.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.860 total time= 5.5s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.819 total time= 2.7s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.857 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.838 total time= 2.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.884 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.858 total time= 2.7s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.814 total time= 0.6s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.866 total time= 0.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.851 total time= 0.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.848 total time= 0.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.846 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.821 total time= 5.3s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.815 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.859 total time= 5.2s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.866 total time= 1.4s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.857 total time= 1.4s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.849 total time= 5.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.858 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.845 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.857 total time= 5.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.854 total time= 5.2s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.817 total time= 2.9s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.863 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.846 total time= 2.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.859 total time= 2.8s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.847 total time= 2.8s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.816 total time= 0.6s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.874 total time= 0.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.856 total time= 0.8s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.852 total time= 0.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.857 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.814 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.822 total time= 5.3s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.871 total time= 5.5s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.854 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.842 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.861 total time= 1.5s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.844 total time= 5.4s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.863 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.849 total time= 5.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.857 total time= 5.4s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.820 total time= 2.8s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.875 total time= 2.8s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.846 total time= 2.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.862 total time= 2.8s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.852 total time= 2.9s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.821 total time= 0.7s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.874 total time= 0.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.838 total time= 0.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.882 total time= 0.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.842 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.816 total time= 1.3s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.819 total time= 5.2s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.878 total time= 5.5s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.843 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.852 total time= 5.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.863 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.834 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.860 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.856 total time= 5.1s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.853 total time= 5.2s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.819 total time= 2.5s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.859 total time= 2.6s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.839 total time= 2.5s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.865 total time= 2.6s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.854 total time= 2.4s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.822 total time= 0.7s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.859 total time= 0.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.840 total time= 0.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.863 total time= 0.6s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.863 total time= 0.6s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.824 total time= 1.2s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.828 total time= 5.1s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.863 total time= 4.9s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.865 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.849 total time= 5.0s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.847 total time= 1.4s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.868 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.865 total time= 1.4s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.856 total time= 5.2s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.848 total time= 5.2s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.815 total time= 2.6s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.860 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.854 total time= 2.6s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.868 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.863 total time= 2.7s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.868 total time= 1.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.921 total time= 1.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.891 total time= 1.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.928 total time= 1.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.938 total time= 0.9s
[CV 1/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.819 total time= 5.6s
[CV 2/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.869 total time= 5.5s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.862 total time= 2.1s
[CV 3/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.848 total time= 5.6s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.931 total time= 2.2s
[CV 5/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.853 total time= 5.7s
[CV 4/5] END bootstrap=True, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.858 total time= 6.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.918 total time= 2.2s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.923 total time= 2.2s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.924 total time= 2.2s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.860 total time= 4.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.921 total time= 4.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.938 total time= 4.2s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.919 total time= 4.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.927 total time= 4.3s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.867 total time= 1.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.938 total time= 1.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.926 total time= 0.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.919 total time= 1.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.926 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.863 total time= 2.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.862 total time= 8.1s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.935 total time= 8.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.942 total time= 2.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.916 total time= 1.9s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.925 total time= 1.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.919 total time= 1.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.921 total time= 8.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.923 total time= 7.7s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.931 total time= 8.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.859 total time= 4.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.936 total time= 3.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.931 total time= 3.9s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.924 total time= 3.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.923 total time= 3.9s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.863 total time= 1.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.940 total time= 1.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.915 total time= 1.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.934 total time= 1.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.926 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.861 total time= 2.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.859 total time= 8.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.936 total time= 2.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.934 total time= 8.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.936 total time= 2.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.919 total time= 8.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.921 total time= 1.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.918 total time= 2.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.926 total time= 8.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.932 total time= 8.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.861 total time= 4.3s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.939 total time= 4.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.916 total time= 4.2s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.925 total time= 4.3s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.925 total time= 4.3s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.858 total time= 1.1s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.931 total time= 1.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.923 total time= 1.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.931 total time= 1.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.923 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.862 total time= 7.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.932 total time= 8.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.863 total time= 2.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.918 total time= 1.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.922 total time= 2.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.915 total time= 8.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.938 total time= 2.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.927 total time= 2.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.928 total time= 8.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.915 total time= 7.9s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.859 total time= 3.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.935 total time= 3.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.912 total time= 3.9s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.926 total time= 3.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.921 total time= 4.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.860 total time= 1.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.928 total time= 1.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.909 total time= 1.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.928 total time= 1.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.930 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.868 total time= 1.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.928 total time= 2.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.861 total time= 8.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.923 total time= 8.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.937 total time= 1.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.927 total time= 8.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.920 total time= 2.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.917 total time= 2.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.931 total time= 7.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.923 total time= 7.8s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.864 total time= 4.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.937 total time= 4.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.923 total time= 3.8s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.928 total time= 3.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.919 total time= 3.9s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.859 total time= 1.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.922 total time= 0.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.911 total time= 1.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.921 total time= 1.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.923 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.861 total time= 2.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.932 total time= 8.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.861 total time= 8.1s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.936 total time= 2.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.928 total time= 8.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.913 total time= 2.3s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.924 total time= 2.2s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.928 total time= 2.3s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.923 total time= 8.2s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.924 total time= 8.5s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.860 total time= 4.3s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.926 total time= 4.3s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.920 total time= 4.2s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.923 total time= 4.4s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.934 total time= 4.5s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.945 total time= 1.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.862 total time= 1.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.918 total time= 1.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.929 total time= 1.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.932 total time= 1.2s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.866 total time= 2.5s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.931 total time= 8.8s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.862 total time= 9.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.923 total time= 2.4s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.923 total time= 2.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.936 total time= 2.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.922 total time= 8.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.921 total time= 2.2s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.931 total time= 8.7s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.924 total time= 8.8s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.862 total time= 3.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.932 total time= 3.8s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.927 total time= 3.8s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.931 total time= 4.0s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.924 total time= 4.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.857 total time= 1.0s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.922 total time= 1.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.928 total time= 1.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.917 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.919 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.865 total time= 2.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.861 total time= 8.3s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.924 total time= 8.3s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.938 total time= 2.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.917 total time= 2.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.930 total time= 8.5s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.928 total time= 2.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.928 total time= 2.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.922 total time= 8.3s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.922 total time= 8.3s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.865 total time= 3.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.924 total time= 4.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.928 total time= 3.9s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.935 total time= 3.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.928 total time= 3.8s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.861 total time= 0.9s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.924 total time= 0.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.910 total time= 1.0s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.932 total time= 0.9s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.931 total time= 1.0s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.858 total time= 1.9s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.862 total time= 7.7s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.930 total time= 7.7s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.927 total time= 2.1s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.913 total time= 1.9s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.928 total time= 7.8s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.934 total time= 2.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.915 total time= 2.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.925 total time= 7.7s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.925 total time= 8.1s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.858 total time= 4.2s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.935 total time= 4.2s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.919 total time= 4.1s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.919 total time= 4.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.929 total time= 4.2s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.983 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.869 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.5s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.981 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.861 total time= 8.4s
[CV 2/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.931 total time= 8.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.869 total time= 3.0s
[CV 3/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.918 total time= 8.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.983 total time= 2.8s
[CV 5/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.922 total time= 8.1s
[CV 4/5] END bootstrap=True, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.928 total time= 8.4s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.0s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.982 total time= 2.9s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.982 total time= 2.9s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.870 total time= 5.8s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.983 total time= 5.7s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.981 total time= 5.9s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.982 total time= 5.7s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.983 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.870 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.981 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.867 total time= 2.9s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.870 total time= 11.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.982 total time= 11.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.982 total time= 3.0s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.983 total time= 2.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.982 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.981 total time= 2.7s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.981 total time= 11.4s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.981 total time= 11.2s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.982 total time= 11.9s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.869 total time= 5.7s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.983 total time= 5.9s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.982 total time= 5.9s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.980 total time= 6.0s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.982 total time= 5.8s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.870 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.984 total time= 1.5s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.869 total time= 11.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.870 total time= 2.8s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.983 total time= 11.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.983 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.982 total time= 11.3s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.981 total time= 2.6s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.980 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.981 total time= 2.7s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.982 total time= 11.0s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.983 total time= 10.9s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.868 total time= 5.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.983 total time= 5.6s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.981 total time= 5.5s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.982 total time= 5.6s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.981 total time= 5.6s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.984 total time= 1.2s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.871 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.984 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.979 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.980 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.868 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.869 total time= 11.1s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.983 total time= 11.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.981 total time= 3.0s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.982 total time= 2.9s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.982 total time= 11.8s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.983 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.982 total time= 2.8s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.981 total time= 11.5s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.982 total time= 11.5s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.868 total time= 5.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.984 total time= 5.6s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.982 total time= 5.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.982 total time= 5.6s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.982 total time= 5.6s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.867 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.984 total time= 1.4s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.3s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.981 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.2s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.868 total time= 2.6s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.983 total time= 2.8s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.868 total time= 10.7s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.983 total time= 10.9s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.982 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.982 total time= 11.0s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.982 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.981 total time= 2.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.982 total time= 10.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.981 total time= 10.7s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.869 total time= 5.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.984 total time= 5.7s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.983 total time= 5.8s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.981 total time= 5.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.980 total time= 5.7s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.871 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.3s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.868 total time= 2.6s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.869 total time= 11.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.984 total time= 11.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.981 total time= 2.7s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.981 total time= 2.8s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.982 total time= 11.4s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.980 total time= 2.8s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.982 total time= 2.7s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.982 total time= 11.1s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.981 total time= 11.1s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.868 total time= 5.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.983 total time= 5.8s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.981 total time= 5.5s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.981 total time= 5.5s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.982 total time= 5.7s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.869 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.981 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.980 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.980 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.869 total time= 2.8s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.869 total time= 11.2s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.985 total time= 2.9s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.983 total time= 11.4s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.0s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.980 total time= 2.9s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.983 total time= 11.7s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.981 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.980 total time= 11.8s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.982 total time= 11.9s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.868 total time= 5.9s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.984 total time= 5.7s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.981 total time= 5.7s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.980 total time= 5.8s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.981 total time= 5.6s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.869 total time= 1.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.3s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.981 total time= 1.4s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.979 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.3s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.867 total time= 2.7s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.984 total time= 2.8s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.868 total time= 11.0s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.983 total time= 10.9s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.981 total time= 2.6s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.982 total time= 10.9s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.981 total time= 2.8s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.981 total time= 10.6s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.982 total time= 2.6s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.982 total time= 10.6s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.869 total time= 5.3s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.982 total time= 5.4s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.982 total time= 5.4s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.980 total time= 5.6s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.982 total time= 5.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.985 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.868 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.979 total time= 1.4s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.4s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.868 total time= 2.7s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.869 total time= 11.2s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.984 total time= 11.4s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.982 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.981 total time= 2.9s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.980 total time= 3.0s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.981 total time= 2.8s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.982 total time= 11.8s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.981 total time= 11.5s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.982 total time= 11.7s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.869 total time= 5.9s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.984 total time= 5.8s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.982 total time= 5.8s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.983 total time= 5.7s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.981 total time= 5.7s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.874 total time= 1.7s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.8s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.874 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.869 total time= 11.6s
[CV 2/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.983 total time= 11.8s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.981 total time= 11.4s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.992 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.981 total time= 11.4s
[CV 5/5] END bootstrap=True, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.982 total time= 11.1s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.4s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.874 total time= 6.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 7.1s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.9s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 7.1s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.872 total time= 1.8s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.8s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.8s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.874 total time= 13.4s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.874 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.8s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.992 total time= 3.4s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.5s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 14.8s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.5s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.873 total time= 6.7s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.3s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 7.2s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.0s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.993 total time= 7.4s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.870 total time= 1.7s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.872 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.873 total time= 13.5s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.5s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.2s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.4s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.3s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.872 total time= 6.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.8s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.871 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.873 total time= 12.8s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.872 total time= 3.3s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.8s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.4s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.993 total time= 14.1s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.9s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.873 total time= 6.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.9s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 7.0s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 7.0s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.873 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.9s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.872 total time= 3.5s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.871 total time= 12.9s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.8s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.5s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.9s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.1s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.6s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.9s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.993 total time= 14.2s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.2s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.872 total time= 7.2s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.1s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 7.1s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.9s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.1s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.871 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.991 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.8s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.871 total time= 3.3s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.872 total time= 13.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.1s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.8s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.0s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.870 total time= 6.2s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.5s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.7s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.6s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.9s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.868 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.992 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.868 total time= 2.9s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.871 total time= 12.3s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.3s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.9s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.9s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.869 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.9s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.7s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.869 total time= 1.7s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.8s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.991 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.8s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.869 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.868 total time= 13.3s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.0s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.992 total time= 3.6s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 13.8s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.991 total time= 3.5s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.993 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.992 total time= 13.9s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 13.8s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.870 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.9s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.9s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.992 total time= 6.8s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.868 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.869 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.869 total time= 12.8s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.4s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.1s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.1s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.992 total time= 3.1s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.1s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.2s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.869 total time= 6.2s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.992 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.877 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.876 total time= 3.5s
[CV 1/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.868 total time= 12.4s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 13.1s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.7s
[CV 4/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.992 total time= 12.9s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 13.4s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.877 total time= 6.5s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 7.3s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 7.3s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 7.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 7.2s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.875 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.876 total time= 12.9s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.875 total time= 3.0s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.8s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 14.3s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 14.2s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.874 total time= 6.7s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.0s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 7.1s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 7.3s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.9s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.872 total time= 1.8s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.996 total time= 1.8s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.872 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.875 total time= 13.1s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.8s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.1s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.9s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.873 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.8s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.6s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.874 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.873 total time= 12.2s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.873 total time= 3.0s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.996 total time= 3.3s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 13.2s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 12.9s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.1s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.872 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.9s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.9s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.873 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.873 total time= 12.2s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.872 total time= 3.0s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.996 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 12.9s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.0s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.1s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 12.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 12.8s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.873 total time= 5.9s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.7s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.8s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.9s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.872 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.8s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.9s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.871 total time= 3.5s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.873 total time= 13.3s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.1s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.4s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.5s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.5s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 14.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.5s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.3s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.871 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.8s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 7.0s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.7s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.9s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.869 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.992 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.870 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.872 total time= 12.5s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.996 total time= 13.2s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.5s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.992 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.993 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.3s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.870 total time= 6.6s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.8s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.9s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.8s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.869 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.8s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.992 total time= 1.4s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.870 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.870 total time= 12.4s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.0s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 12.9s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.993 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.992 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 13.4s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.870 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 7.1s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 7.1s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.992 total time= 7.2s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.9s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.868 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.9s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.9s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.991 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.8s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.869 total time= 3.5s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.5s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.870 total time= 13.8s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.4s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 14.3s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.9s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 14.1s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.869 total time= 6.1s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.3s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.992 total time= 6.4s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.5s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.875 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.875 total time= 3.1s
[CV 1/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.869 total time= 12.0s
[CV 2/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.4s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.7s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.5s
[CV 5/5] END bootstrap=True, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.3s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.877 total time= 6.1s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.6s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.7s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.875 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.875 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.876 total time= 12.3s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.4s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.4s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.3s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.875 total time= 6.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.5s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.873 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.873 total time= 3.0s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.875 total time= 12.0s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.996 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.4s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.2s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.872 total time= 6.3s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.5s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.5s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.875 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.873 total time= 3.3s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.873 total time= 12.4s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.996 total time= 3.3s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 13.1s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 13.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.4s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.873 total time= 6.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.3s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.873 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.872 total time= 3.1s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.873 total time= 12.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 12.8s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.0s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.0s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 12.7s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.873 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.3s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.4s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.872 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.871 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.872 total time= 12.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.996 total time= 12.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.1s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 12.9s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.0s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 12.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 12.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.873 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.996 total time= 6.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.3s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.5s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.870 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.992 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.870 total time= 3.2s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.872 total time= 12.2s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 12.8s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.0s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.9s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.992 total time= 3.1s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.1s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.6s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.8s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.871 total time= 5.9s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.1s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 6.3s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.992 total time= 6.4s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.2s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.868 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.5s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.870 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.869 total time= 11.8s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 12.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 12.3s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.993 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.992 total time= 3.1s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.992 total time= 3.1s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 12.4s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 12.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.871 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.3s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.992 total time= 6.3s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.4s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.871 total time= 1.4s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.991 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.869 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.870 total time= 12.2s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 12.5s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.0s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.991 total time= 3.1s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 12.6s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 12.5s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 12.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.869 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.2s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.2s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.2s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.1s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.875 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.0s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.869 total time= 12.2s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.875 total time= 3.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.3s
[CV 2/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 12.9s
[CV 3/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.4s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.992 total time= 12.7s
[CV 5/5] END bootstrap=True, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.7s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.877 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.6s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.7s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.875 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.876 total time= 12.5s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.875 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.996 total time= 3.2s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.3s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.5s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.875 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.6s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.873 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.874 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.875 total time= 12.1s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.3s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.1s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.2s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.1s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.0s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.0s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.873 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.7s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 6.4s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.873 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.992 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.873 total time= 3.0s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.873 total time= 12.1s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 12.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 12.8s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 3.3s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.9s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.874 total time= 6.2s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.5s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 6.3s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.875 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.992 total time= 1.7s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.9s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.873 total time= 12.1s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.874 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.3s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.3s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.0s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.4s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 13.0s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.873 total time= 6.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.996 total time= 6.4s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.4s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 6.5s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.872 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 1.5s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.871 total time= 2.9s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.1s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.873 total time= 12.2s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 12.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 12.9s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.2s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 12.8s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 12.6s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.872 total time= 5.9s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.3s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.2s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.3s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.870 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.992 total time= 1.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 1.8s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 1.7s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.871 total time= 3.1s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.871 total time= 12.1s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.1s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.996 total time= 13.0s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 3.5s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 3.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.993 total time= 13.7s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 13.7s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.870 total time= 7.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 7.0s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 7.2s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.992 total time= 7.2s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.993 total time= 7.2s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.870 total time= 1.6s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.991 total time= 1.9s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 1.8s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.870 total time= 3.5s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.870 total time= 13.4s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 3.7s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 14.5s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 14.0s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.993 total time= 3.4s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.992 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.993 total time= 3.3s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 13.8s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.993 total time= 14.2s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.870 total time= 6.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 6.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 7.0s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.992 total time= 6.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 6.7s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.869 total time= 1.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.7s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.7s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.992 total time= 1.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 1.6s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.869 total time= 3.4s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.870 total time= 12.8s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 3.4s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 13.4s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.6s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 13.7s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.992 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 3.6s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 13.6s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.992 total time= 14.1s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.869 total time= 7.0s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.9s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 6.8s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 7.0s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 6.7s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.876 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.1s
[CV 1/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.869 total time= 12.5s
[CV 2/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 12.7s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.877 total time= 4.0s
[CV 3/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.8s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.991 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.5s
[CV 4/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 12.6s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.5s
[CV 5/5] END bootstrap=True, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 12.7s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.6s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.877 total time= 8.0s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.992 total time= 8.9s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.992 total time= 9.2s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.1s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.877 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.992 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.876 total time= 17.1s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.877 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.992 total time= 18.6s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.9s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.9s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.9s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.992 total time= 18.5s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.7s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 19.0s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 18.7s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.876 total time= 8.4s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.1s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.4s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.993 total time= 9.2s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.996 total time= 9.3s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.874 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.875 total time= 4.3s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.876 total time= 16.8s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.7s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.8s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.9s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.4s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.874 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.6s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.1s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.874 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.874 total time= 16.7s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.0s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.8s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.6s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.875 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.9s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 9.0s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.875 total time= 2.2s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.1s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.875 total time= 16.7s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 18.2s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.2s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.7s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 18.2s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.1s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.875 total time= 8.3s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.993 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.873 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.873 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.874 total time= 16.5s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.1s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.993 total time= 4.5s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.5s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.5s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.873 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.6s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.6s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.872 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.4s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.873 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.873 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.5s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.1s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 18.2s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.6s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.872 total time= 8.4s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.8s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.872 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.872 total time= 4.4s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.872 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.9s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.5s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.7s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.6s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.6s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.872 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.7s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.7s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.871 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.872 total time= 16.6s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.872 total time= 4.0s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.1s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.6s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.9s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.871 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.6s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.813 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.6s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.851 total time= 0.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.856 total time= 0.7s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.863 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.821 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.811 total time= 1.6s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.871 total time= 1.6s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.837 total time= 1.5s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.857 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.845 total time= 1.6s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.812 total time= 3.2s
[CV 1/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.871 total time= 16.5s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.863 total time= 3.2s
[CV 2/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.9s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.852 total time= 3.1s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.849 total time= 3.1s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.852 total time= 3.3s
[CV 3/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 4/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 18.0s
[CV 5/5] END bootstrap=False, max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.2s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.815 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.814 total time= 6.3s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.879 total time= 6.0s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.858 total time= 0.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.834 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.831 total time= 0.7s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.863 total time= 0.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.846 total time= 6.1s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.849 total time= 6.0s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.815 total time= 1.5s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.862 total time= 1.5s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.844 total time= 1.4s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.851 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.844 total time= 6.0s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.846 total time= 1.4s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.819 total time= 2.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.873 total time= 2.9s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.852 total time= 3.0s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.850 total time= 3.0s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.854 total time= 3.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.818 total time= 0.8s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.872 total time= 0.8s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.854 total time= 0.6s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.853 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.852 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.816 total time= 1.4s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.815 total time= 6.0s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.869 total time= 6.0s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.854 total time= 1.5s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.847 total time= 1.6s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.855 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.848 total time= 1.6s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.861 total time= 6.0s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.846 total time= 6.3s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.847 total time= 5.9s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.813 total time= 3.2s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.877 total time= 3.0s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.848 total time= 3.0s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.856 total time= 3.0s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.855 total time= 3.0s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.815 total time= 0.7s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.851 total time= 0.8s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.852 total time= 0.8s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.858 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.869 total time= 0.7s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.863 total time= 6.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.815 total time= 1.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.818 total time= 6.4s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.875 total time= 1.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.847 total time= 6.1s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.869 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.845 total time= 1.4s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.864 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.853 total time= 6.2s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.859 total time= 6.3s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.817 total time= 2.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.871 total time= 3.1s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.840 total time= 2.9s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.852 total time= 2.9s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.854 total time= 3.0s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.816 total time= 0.8s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.857 total time= 0.8s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.863 total time= 0.7s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.855 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.822 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.817 total time= 1.5s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.867 total time= 5.9s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.811 total time= 6.0s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.868 total time= 1.4s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.849 total time= 5.9s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.857 total time= 1.4s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.867 total time= 1.4s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.856 total time= 1.5s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.855 total time= 6.0s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.845 total time= 5.9s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.816 total time= 2.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.862 total time= 2.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.839 total time= 2.9s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.856 total time= 2.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.857 total time= 2.9s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.819 total time= 0.7s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.871 total time= 0.8s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.873 total time= 0.7s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.862 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.856 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.816 total time= 1.5s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.856 total time= 1.5s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.816 total time= 5.8s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.866 total time= 5.9s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.847 total time= 5.8s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.840 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.860 total time= 1.4s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.844 total time= 1.5s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.855 total time= 5.9s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.847 total time= 5.9s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.818 total time= 3.0s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.867 total time= 2.9s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.844 total time= 2.9s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.845 total time= 3.0s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.865 total time= 3.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.812 total time= 0.7s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.857 total time= 0.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.877 total time= 0.7s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.871 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.830 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.815 total time= 1.5s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.812 total time= 5.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.865 total time= 5.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.868 total time= 1.5s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.847 total time= 5.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.828 total time= 1.4s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.859 total time= 1.4s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.869 total time= 1.4s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.864 total time= 5.8s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.858 total time= 5.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.822 total time= 3.0s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.854 total time= 3.0s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.850 total time= 3.0s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.855 total time= 3.2s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.827 total time= 3.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.820 total time= 0.8s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.837 total time= 0.8s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.864 total time= 0.7s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.868 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.843 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.809 total time= 1.4s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.873 total time= 6.0s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.815 total time= 6.1s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.865 total time= 1.6s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.841 total time= 1.5s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.855 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.845 total time= 1.4s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.848 total time= 6.1s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.852 total time= 5.9s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.855 total time= 6.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.819 total time= 2.8s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.865 total time= 3.0s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.849 total time= 2.9s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.845 total time= 3.0s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.860 total time= 3.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.811 total time= 0.8s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.884 total time= 0.8s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.860 total time= 0.7s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.871 total time= 0.7s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.829 total time= 0.7s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.810 total time= 1.4s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.816 total time= 5.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.863 total time= 5.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.854 total time= 1.5s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.843 total time= 1.6s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.847 total time= 6.1s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.852 total time= 1.5s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.844 total time= 1.5s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.863 total time= 5.9s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.844 total time= 5.8s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.813 total time= 2.9s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.861 total time= 3.0s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.830 total time= 3.0s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.856 total time= 3.0s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.858 total time= 3.1s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.927 total time= 1.1s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.860 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.915 total time= 1.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.918 total time= 1.3s
[CV 2/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.860 total time= 6.1s
[CV 1/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.813 total time= 6.1s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.935 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.845 total time= 6.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.861 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.844 total time= 5.9s
[CV 4/5] END bootstrap=False, max_depth=2, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.854 total time= 6.1s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.929 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.916 total time= 2.5s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.934 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.922 total time= 2.4s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.858 total time= 5.0s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.932 total time= 4.9s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.934 total time= 5.1s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.933 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.915 total time= 5.0s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.859 total time= 1.2s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.916 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.908 total time= 1.2s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.930 total time= 1.3s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.912 total time= 1.3s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.859 total time= 2.5s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.856 total time= 10.0s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.928 total time= 9.6s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.940 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.922 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.933 total time= 2.6s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.914 total time= 2.7s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.923 total time= 9.9s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.931 total time= 9.9s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.924 total time= 10.0s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.858 total time= 5.4s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.937 total time= 5.3s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.921 total time= 5.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.924 total time= 5.0s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.924 total time= 5.1s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.861 total time= 1.2s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.921 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.926 total time= 1.2s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.913 total time= 1.2s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.922 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.861 total time= 2.5s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.859 total time= 9.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.932 total time= 9.7s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.935 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.924 total time= 9.7s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.919 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.917 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.925 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.926 total time= 9.6s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.921 total time= 9.4s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.862 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.914 total time= 4.9s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.918 total time= 4.7s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.941 total time= 5.1s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.928 total time= 4.9s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.859 total time= 1.1s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.922 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.936 total time= 1.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.925 total time= 1.2s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.929 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.860 total time= 2.5s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.862 total time= 9.9s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.933 total time= 9.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.933 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.916 total time= 2.6s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.922 total time= 10.0s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.929 total time= 2.6s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.910 total time= 2.5s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.929 total time= 9.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.921 total time= 9.9s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.858 total time= 5.2s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.935 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.909 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.930 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.918 total time= 4.9s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.859 total time= 1.2s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.924 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.931 total time= 1.2s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.929 total time= 1.2s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.917 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.857 total time= 2.6s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.928 total time= 2.5s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.858 total time= 9.7s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.934 total time= 9.7s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.930 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.918 total time= 9.9s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.926 total time= 2.5s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.924 total time= 2.5s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.924 total time= 9.7s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.918 total time= 9.6s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.860 total time= 4.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.920 total time= 4.9s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.917 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.929 total time= 4.9s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.925 total time= 4.8s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.859 total time= 1.1s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.933 total time= 1.1s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.936 total time= 1.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.906 total time= 1.3s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.903 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.862 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.859 total time= 10.0s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.921 total time= 9.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.936 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.912 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.921 total time= 9.6s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.934 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.922 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.924 total time= 9.5s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.921 total time= 9.6s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.859 total time= 4.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.935 total time= 4.7s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.929 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.928 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.920 total time= 5.0s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.857 total time= 1.3s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.933 total time= 1.1s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.913 total time= 1.3s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.931 total time= 1.3s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.927 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.861 total time= 2.6s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.861 total time= 10.0s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.919 total time= 9.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.926 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.919 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.925 total time= 9.7s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.932 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.916 total time= 2.5s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.927 total time= 9.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.914 total time= 9.9s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.860 total time= 4.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.936 total time= 5.0s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.923 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.934 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.920 total time= 4.9s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.854 total time= 1.2s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.915 total time= 1.3s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.938 total time= 1.2s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.926 total time= 1.2s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.914 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.859 total time= 2.5s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.940 total time= 9.3s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.860 total time= 9.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.927 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.920 total time= 9.6s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.930 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.928 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.932 total time= 2.7s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.929 total time= 9.6s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.916 total time= 9.5s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.862 total time= 4.7s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.937 total time= 4.7s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.912 total time= 4.7s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.934 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.909 total time= 4.8s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.859 total time= 1.3s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.931 total time= 1.4s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.913 total time= 1.2s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.929 total time= 1.2s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.925 total time= 1.2s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.859 total time= 2.5s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.936 total time= 9.8s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.931 total time= 2.5s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.858 total time= 10.3s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.901 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.924 total time= 10.1s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.925 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.915 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.929 total time= 10.1s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.921 total time= 9.9s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.858 total time= 4.7s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.925 total time= 4.9s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.924 total time= 4.9s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.924 total time= 4.7s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.920 total time= 4.7s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.870 total time= 1.8s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.983 total time= 1.8s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.9s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.983 total time= 1.7s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.860 total time= 9.7s
[CV 2/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.934 total time= 9.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.869 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.928 total time= 9.9s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.984 total time= 3.4s
[CV 4/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.929 total time= 9.8s
[CV 5/5] END bootstrap=False, max_depth=4, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.922 total time= 9.7s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.981 total time= 3.4s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.6s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.5s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.868 total time= 7.0s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.983 total time= 6.8s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.982 total time= 7.0s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.982 total time= 7.0s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.982 total time= 6.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.869 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.9s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.9s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.9s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.979 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.868 total time= 13.9s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.867 total time= 3.5s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.984 total time= 14.4s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.983 total time= 3.7s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.982 total time= 3.5s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.981 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.1s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.981 total time= 3.6s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.5s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.4s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.868 total time= 7.2s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.984 total time= 7.0s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.982 total time= 6.9s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.981 total time= 7.2s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.982 total time= 7.2s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.870 total time= 1.7s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.983 total time= 1.8s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.983 total time= 1.9s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.7s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.868 total time= 3.5s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.868 total time= 14.2s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.983 total time= 14.3s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.984 total time= 3.5s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.983 total time= 3.5s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.983 total time= 14.2s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.982 total time= 3.6s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.982 total time= 14.3s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.983 total time= 3.6s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.981 total time= 14.3s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.870 total time= 7.2s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.982 total time= 7.1s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.984 total time= 7.4s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.981 total time= 7.0s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.982 total time= 7.0s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.869 total time= 1.6s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.983 total time= 1.7s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.983 total time= 1.8s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.8s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.981 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.869 total time= 3.6s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.868 total time= 14.2s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.984 total time= 14.4s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.983 total time= 3.5s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.981 total time= 3.5s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.983 total time= 14.1s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.6s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.981 total time= 14.2s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.982 total time= 14.4s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.868 total time= 7.2s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.983 total time= 7.1s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.983 total time= 7.3s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.981 total time= 7.0s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.981 total time= 7.5s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.868 total time= 1.8s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.984 total time= 1.7s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.9s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.981 total time= 1.8s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.868 total time= 3.7s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.868 total time= 14.4s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.983 total time= 3.6s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.983 total time= 14.2s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.983 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.983 total time= 14.3s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.980 total time= 3.8s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.982 total time= 3.5s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.981 total time= 14.1s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.3s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.868 total time= 6.9s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.983 total time= 7.0s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.982 total time= 7.1s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.981 total time= 6.9s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.982 total time= 7.1s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.869 total time= 1.7s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.984 total time= 1.8s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.8s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.8s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.868 total time= 3.6s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.869 total time= 14.3s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.983 total time= 14.2s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.985 total time= 3.5s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.983 total time= 14.2s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.982 total time= 3.6s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.982 total time= 3.6s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.981 total time= 3.4s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.981 total time= 14.0s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.982 total time= 13.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.869 total time= 7.0s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.984 total time= 7.0s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.982 total time= 7.2s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.982 total time= 7.0s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.981 total time= 7.2s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.869 total time= 1.8s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.983 total time= 1.9s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.984 total time= 1.8s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.980 total time= 1.8s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.982 total time= 1.9s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.870 total time= 3.5s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.869 total time= 14.3s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.984 total time= 14.4s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.983 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.982 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.982 total time= 14.3s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.983 total time= 3.5s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.980 total time= 3.7s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.981 total time= 14.6s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.981 total time= 14.3s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.870 total time= 7.2s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.983 total time= 6.9s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.983 total time= 7.0s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.984 total time= 7.3s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.982 total time= 7.0s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.870 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.8s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.983 total time= 1.8s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.981 total time= 1.7s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.982 total time= 1.7s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.867 total time= 3.5s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.983 total time= 13.9s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.868 total time= 14.1s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.983 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.983 total time= 3.5s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.984 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.6s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.983 total time= 3.8s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.1s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.982 total time= 14.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.868 total time= 7.1s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.983 total time= 7.1s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.981 total time= 7.1s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.983 total time= 7.4s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.982 total time= 7.2s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.870 total time= 1.8s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.981 total time= 1.9s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.7s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.982 total time= 1.9s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.979 total time= 1.8s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.869 total time= 3.6s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.868 total time= 14.7s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.984 total time= 14.3s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.983 total time= 3.6s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.981 total time= 14.5s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.981 total time= 3.6s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.981 total time= 3.5s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.981 total time= 3.4s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.982 total time= 14.0s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.982 total time= 14.1s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.868 total time= 6.9s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.984 total time= 7.1s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.983 total time= 7.1s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.982 total time= 7.0s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.983 total time= 7.3s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.875 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.7s
[CV 1/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.868 total time= 14.5s
[CV 2/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.984 total time= 14.7s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.875 total time= 4.3s
[CV 3/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.983 total time= 14.6s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.981 total time= 14.7s
[CV 5/5] END bootstrap=False, max_depth=8, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.983 total time= 14.7s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.875 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.0s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.1s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.3s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.3s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.874 total time= 2.3s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.875 total time= 16.8s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.874 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 19.3s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 5.3s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 5.4s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 19.1s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 19.8s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 19.8s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.875 total time= 13.7s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 14.4s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 14.5s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.993 total time= 14.6s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 14.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.873 total time= 2.2s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.6s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.873 total time= 4.7s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.874 total time= 23.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 24.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 19.9s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 19.3s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 19.3s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.873 total time= 8.4s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.1s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.2s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.873 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.0s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.873 total time= 16.7s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.872 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 18.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.8s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.873 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.9s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.7s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.873 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.873 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.873 total time= 16.3s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.8s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.7s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.873 total time= 8.3s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.5s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.9s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.872 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.1s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.872 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.873 total time= 16.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.7s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.4s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.8s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.872 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.6s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.993 total time= 8.8s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.871 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.871 total time= 4.0s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.872 total time= 15.9s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.3s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.5s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.4s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.3s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.871 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.6s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.6s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.871 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.871 total time= 4.0s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.871 total time= 15.9s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.3s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.2s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.2s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.871 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.4s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.6s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.993 total time= 8.5s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.871 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.870 total time= 4.0s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.871 total time= 16.6s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.2s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.1s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.0s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.3s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.993 total time= 17.1s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.4s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.871 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.8s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.7s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 8.6s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.7s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.877 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.4s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.876 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.871 total time= 16.5s
[CV 2/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.1s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.992 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.992 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.7s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.2s
[CV 4/5] END bootstrap=False, max_depth=16, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.993 total time= 17.3s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.877 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.992 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.1s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.992 total time= 9.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.876 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.5s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.4s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.876 total time= 17.0s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.876 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 18.9s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.7s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.7s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.992 total time= 19.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.992 total time= 19.5s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 19.8s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.876 total time= 8.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.993 total time= 9.1s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.1s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.874 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.876 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.6s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.993 total time= 18.0s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.996 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.4s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.875 total time= 8.4s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.8s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.875 total time= 2.2s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.876 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.874 total time= 16.5s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 18.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.6s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.4s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 18.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.2s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.875 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.993 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 9.1s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.875 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.875 total time= 4.3s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.875 total time= 16.6s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.9s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.2s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.4s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.875 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.8s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.7s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.873 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.6s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.875 total time= 16.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.874 total time= 4.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.8s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.7s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.5s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.7s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.0s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.874 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.871 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.872 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.873 total time= 16.6s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.0s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.871 total time= 8.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.5s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.7s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.872 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.872 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.872 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.3s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.2s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.1s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.5s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.4s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.872 total time= 8.0s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.6s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.872 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.871 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.871 total time= 16.1s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.2s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.8s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.3s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.7s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.872 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.876 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.4s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.877 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.871 total time= 16.3s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.992 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.4s
[CV 5/5] END bootstrap=False, max_depth=32, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.5s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.877 total time= 8.6s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 8.9s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.992 total time= 9.1s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.1s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.876 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.877 total time= 16.6s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.876 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.992 total time= 18.4s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.993 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 18.5s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 18.7s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 19.0s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.876 total time= 8.3s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.993 total time= 9.3s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.993 total time= 9.3s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.875 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.1s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.876 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.993 total time= 18.7s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.6s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.5s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.5s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.7s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.875 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.1s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.874 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.874 total time= 16.5s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.9s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 5.0s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.3s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.9s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 18.8s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.4s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.875 total time= 9.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.4s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.0s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 9.3s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.875 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.875 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.993 total time= 17.9s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.0s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.9s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.875 total time= 8.5s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.1s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.8s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.873 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.1s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.874 total time= 4.0s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.875 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.7s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.1s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.0s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.8s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.873 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.9s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.7s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.871 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.873 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.873 total time= 16.6s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.5s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.872 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 9.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.6s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.9s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.7s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.871 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.872 total time= 4.5s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.872 total time= 17.0s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.6s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 18.0s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.0s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.4s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.7s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.9s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.871 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.7s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.9s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.872 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.0s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.871 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.872 total time= 16.3s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.3s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.2s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.5s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.2s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.4s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.3s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.871 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.6s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.5s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.877 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=0.992 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.878 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.871 total time= 16.3s
[CV 2/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.5s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.992 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.7s
[CV 3/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.8s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=0.993 total time= 4.6s
[CV 4/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.7s
[CV 5/5] END bootstrap=False, max_depth=64, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.6s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.876 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.992 total time= 9.3s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.993 total time= 9.4s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.3s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.876 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.992 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.4s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.993 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.876 total time= 16.9s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.876 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.7s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.992 total time= 18.8s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.8s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 18.9s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.7s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.7s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.994 total time= 18.9s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=2, n_estimators=800;, score=0.993 total time= 19.5s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.876 total time= 8.6s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.991 total time= 9.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.1s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.875 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 2.3s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.993 total time= 2.3s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.874 total time= 4.0s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.876 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.993 total time= 18.0s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=0.996 total time= 4.5s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=5, n_estimators=800;, score=0.994 total time= 18.4s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.874 total time= 8.5s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.994 total time= 8.9s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.9s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.875 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.4s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.875 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.874 total time= 16.5s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.8s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.994 total time= 18.1s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=1, min_samples_split=10, n_estimators=800;, score=0.995 total time= 18.0s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.875 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 9.0s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.9s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.9s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=400;, score=0.995 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.875 total time= 1.9s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=0.996 total time= 2.3s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.874 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.875 total time= 16.3s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.8s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.995 total time= 18.1s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.3s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=2, n_estimators=800;, score=0.994 total time= 18.0s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.875 total time= 8.4s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.9s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.994 total time= 9.0s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 9.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=400;, score=0.995 total time= 9.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.873 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.872 total time= 4.0s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.875 total time= 16.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.994 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.8s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.3s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.8s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.4s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.873 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.0s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.994 total time= 9.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=400;, score=0.995 total time= 9.0s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.871 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.995 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.1s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.993 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=0.994 total time= 2.2s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.872 total time= 4.2s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.873 total time= 16.4s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.3s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.994 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.9s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.994 total time= 4.3s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=0.995 total time= 4.2s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.8s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=2, min_samples_split=10, n_estimators=800;, score=0.995 total time= 17.8s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.872 total time= 8.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.5s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.994 total time= 8.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.5s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=400;, score=0.995 total time= 8.6s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.872 total time= 2.0s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.1s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.996 total time= 2.1s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.994 total time= 2.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=0.995 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.872 total time= 4.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.872 total time= 15.8s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.2s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.995 total time= 4.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.7s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=0.994 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.994 total time= 17.4s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=2, n_estimators=800;, score=0.995 total time= 17.2s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.871 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.6s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.994 total time= 8.6s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=400;, score=0.995 total time= 8.7s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.871 total time= 2.1s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.995 total time= 2.2s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.1s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=0.994 total time= 2.1s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.871 total time= 4.3s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.872 total time= 16.6s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.7s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.6s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.5s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 18.1s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.993 total time= 4.6s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=0.995 total time= 4.4s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.994 total time= 17.9s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=5, n_estimators=800;, score=0.995 total time= 17.7s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.871 total time= 8.2s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.7s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.993 total time= 8.4s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=400;, score=0.995 total time= 8.8s
[CV 1/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.871 total time= 14.8s
[CV 2/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 16.1s
[CV 3/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 15.7s
[CV 4/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.994 total time= 15.2s
[CV 5/5] END bootstrap=False, max_depth=128, min_samples_leaf=4, min_samples_split=10, n_estimators=800;, score=0.995 total time= 15.0s
Best parameters: {'bootstrap': False, 'max_depth': 32, 'min_samples_leaf': 1, 'min_samples_split': 10, 'n_estimators': 200}
Precision: 0.7929777098159149
Recall: 0.013245033112582788
F1 Score: 0.026054874080830417
Weighted accuracy: 0.4867051755266379
--------------------
Classification Report:
precision recall f1-score support
0 0.48 1.00 0.65 1413.6493732849112
1 0.79 0.01 0.03 1521.4751420454538
accuracy 0.49 2935.1245153303653
macro avg 0.64 0.50 0.34 2935.1245153303653
weighted avg 0.64 0.49 0.33 2935.1245153303653
Confusion Matrix:
[[1408.38830379 5.26106949]
[1501.32315341 20.15198864]]
--------------------
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(rf_grid_search, "max_depth", 'n_estimators')
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(rf_grid_search, "min_samples_split", 'min_samples_leaf')
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(rf_grid_search, "n_estimators", 'min_samples_split')
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(rf_grid_search, "n_estimators", 'min_samples_leaf')
plot_gridsearch_2d_hyperparameter_accuracy_heatmap(rf_grid_search, "n_estimators", 'bootstrap')
import joblib
joblib.dump(rf_grid_search.best_estimator_, 'models/rf_best_model.joblib')
['rf_best_model.joblib']
from sklearn.neighbors import KNeighborsClassifier
knn_model = knn = KNeighborsClassifier()
knn_param_grid = {
'n_neighbors': list(range(1, 100)),
}
knn_grid_search = do_grid_search_full_task(knn_model, knn_param_grid)
Fitting 5 folds for each of 99 candidates, totalling 495 fits
[CV 1/5] END .....................n_neighbors=1;, score=0.741 total time= 0.5s
[CV 4/5] END .....................n_neighbors=1;, score=0.739 total time= 0.4s
[CV 2/5] END .....................n_neighbors=1;, score=0.722 total time= 0.5s
[CV 3/5] END .....................n_neighbors=1;, score=0.733 total time= 0.5s
[CV 5/5] END .....................n_neighbors=1;, score=0.738 total time= 0.3s
[CV 1/5] END .....................n_neighbors=2;, score=0.736 total time= 0.4s
[CV 2/5] END .....................n_neighbors=2;, score=0.725 total time= 0.4s
[CV 3/5] END .....................n_neighbors=2;, score=0.733 total time= 0.5s
[CV 4/5] END .....................n_neighbors=2;, score=0.731 total time= 0.3s
[CV 5/5] END .....................n_neighbors=2;, score=0.735 total time= 0.4s
[CV 1/5] END .....................n_neighbors=3;, score=0.708 total time= 0.4s
[CV 2/5] END .....................n_neighbors=3;, score=0.703 total time= 0.4s
[CV 4/5] END .....................n_neighbors=3;, score=0.710 total time= 0.3s
[CV 3/5] END .....................n_neighbors=3;, score=0.705 total time= 0.4s
[CV 5/5] END .....................n_neighbors=3;, score=0.714 total time= 0.4s
[CV 3/5] END .....................n_neighbors=4;, score=0.711 total time= 0.5s
[CV 2/5] END .....................n_neighbors=4;, score=0.705 total time= 0.5s
[CV 1/5] END .....................n_neighbors=4;, score=0.700 total time= 0.6s
[CV 4/5] END .....................n_neighbors=4;, score=0.710 total time= 0.5s
[CV 5/5] END .....................n_neighbors=4;, score=0.711 total time= 0.4s
[CV 1/5] END .....................n_neighbors=5;, score=0.690 total time= 0.4s
[CV 3/5] END .....................n_neighbors=5;, score=0.694 total time= 0.4s
[CV 2/5] END .....................n_neighbors=5;, score=0.686 total time= 0.5s
[CV 4/5] END .....................n_neighbors=5;, score=0.694 total time= 0.5s
[CV 5/5] END .....................n_neighbors=5;, score=0.698 total time= 0.5s
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[CV 2/5] END ....................n_neighbors=56;, score=0.661 total time= 0.4s
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[CV 3/5] END ....................n_neighbors=56;, score=0.651 total time= 0.5s
[CV 5/5] END ....................n_neighbors=56;, score=0.661 total time= 0.5s
[CV 1/5] END ....................n_neighbors=57;, score=0.646 total time= 0.5s
[CV 2/5] END ....................n_neighbors=57;, score=0.656 total time= 0.5s
[CV 3/5] END ....................n_neighbors=57;, score=0.649 total time= 0.6s
[CV 4/5] END ....................n_neighbors=57;, score=0.646 total time= 0.4s
[CV 5/5] END ....................n_neighbors=57;, score=0.661 total time= 0.4s
[CV 1/5] END ....................n_neighbors=58;, score=0.648 total time= 0.5s
[CV 2/5] END ....................n_neighbors=58;, score=0.658 total time= 0.6s
[CV 4/5] END ....................n_neighbors=58;, score=0.649 total time= 0.5s
[CV 3/5] END ....................n_neighbors=58;, score=0.650 total time= 0.5s
[CV 5/5] END ....................n_neighbors=58;, score=0.662 total time= 0.4s
[CV 2/5] END ....................n_neighbors=59;, score=0.657 total time= 0.4s
[CV 1/5] END ....................n_neighbors=59;, score=0.646 total time= 0.5s
[CV 3/5] END ....................n_neighbors=59;, score=0.648 total time= 0.5s
[CV 4/5] END ....................n_neighbors=59;, score=0.645 total time= 0.4s
[CV 5/5] END ....................n_neighbors=59;, score=0.662 total time= 0.4s
[CV 2/5] END ....................n_neighbors=60;, score=0.662 total time= 0.4s
[CV 1/5] END ....................n_neighbors=60;, score=0.646 total time= 0.5s
[CV 3/5] END ....................n_neighbors=60;, score=0.648 total time= 0.4s
[CV 4/5] END ....................n_neighbors=60;, score=0.649 total time= 0.4s
[CV 5/5] END ....................n_neighbors=60;, score=0.661 total time= 0.4s
[CV 2/5] END ....................n_neighbors=61;, score=0.655 total time= 0.4s
[CV 1/5] END ....................n_neighbors=61;, score=0.646 total time= 0.5s
[CV 3/5] END ....................n_neighbors=61;, score=0.649 total time= 0.4s
[CV 4/5] END ....................n_neighbors=61;, score=0.644 total time= 0.4s
[CV 5/5] END ....................n_neighbors=61;, score=0.662 total time= 0.4s
[CV 1/5] END ....................n_neighbors=62;, score=0.647 total time= 0.5s
[CV 2/5] END ....................n_neighbors=62;, score=0.658 total time= 0.4s
[CV 3/5] END ....................n_neighbors=62;, score=0.648 total time= 0.4s
[CV 4/5] END ....................n_neighbors=62;, score=0.646 total time= 0.4s
[CV 5/5] END ....................n_neighbors=62;, score=0.662 total time= 0.5s
[CV 1/5] END ....................n_neighbors=63;, score=0.646 total time= 0.4s
[CV 2/5] END ....................n_neighbors=63;, score=0.653 total time= 0.4s
[CV 3/5] END ....................n_neighbors=63;, score=0.650 total time= 0.4s
[CV 5/5] END ....................n_neighbors=63;, score=0.661 total time= 0.4s
[CV 4/5] END ....................n_neighbors=63;, score=0.644 total time= 0.5s
[CV 2/5] END ....................n_neighbors=64;, score=0.654 total time= 0.4s
[CV 1/5] END ....................n_neighbors=64;, score=0.645 total time= 0.4s
[CV 3/5] END ....................n_neighbors=64;, score=0.646 total time= 0.5s
[CV 5/5] END ....................n_neighbors=64;, score=0.659 total time= 0.4s
[CV 4/5] END ....................n_neighbors=64;, score=0.648 total time= 0.5s
[CV 1/5] END ....................n_neighbors=65;, score=0.643 total time= 0.5s
[CV 3/5] END ....................n_neighbors=65;, score=0.647 total time= 0.4s
[CV 2/5] END ....................n_neighbors=65;, score=0.653 total time= 0.4s
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[CV 4/5] END ....................n_neighbors=65;, score=0.646 total time= 0.5s
[CV 1/5] END ....................n_neighbors=66;, score=0.643 total time= 0.4s
[CV 2/5] END ....................n_neighbors=66;, score=0.653 total time= 0.4s
[CV 3/5] END ....................n_neighbors=66;, score=0.644 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=67;, score=0.644 total time= 0.4s
[CV 2/5] END ....................n_neighbors=67;, score=0.654 total time= 0.4s
[CV 4/5] END ....................n_neighbors=67;, score=0.646 total time= 0.4s
[CV 3/5] END ....................n_neighbors=67;, score=0.644 total time= 0.5s
[CV 5/5] END ....................n_neighbors=67;, score=0.661 total time= 0.4s
[CV 1/5] END ....................n_neighbors=68;, score=0.644 total time= 0.5s
[CV 2/5] END ....................n_neighbors=68;, score=0.653 total time= 0.4s
[CV 4/5] END ....................n_neighbors=68;, score=0.644 total time= 0.4s
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[CV 5/5] END ....................n_neighbors=68;, score=0.660 total time= 0.4s
[CV 1/5] END ....................n_neighbors=69;, score=0.643 total time= 0.4s
[CV 2/5] END ....................n_neighbors=69;, score=0.651 total time= 0.4s
[CV 3/5] END ....................n_neighbors=69;, score=0.641 total time= 0.5s
[CV 4/5] END ....................n_neighbors=69;, score=0.643 total time= 0.4s
[CV 5/5] END ....................n_neighbors=69;, score=0.660 total time= 0.4s
[CV 1/5] END ....................n_neighbors=70;, score=0.645 total time= 0.5s
[CV 3/5] END ....................n_neighbors=70;, score=0.644 total time= 0.4s
[CV 2/5] END ....................n_neighbors=70;, score=0.654 total time= 0.6s
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[CV 1/5] END ....................n_neighbors=71;, score=0.645 total time= 0.4s
[CV 3/5] END ....................n_neighbors=71;, score=0.638 total time= 0.4s
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[CV 3/5] END ....................n_neighbors=72;, score=0.641 total time= 0.5s
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[CV 2/5] END ....................n_neighbors=73;, score=0.653 total time= 0.5s
[CV 1/5] END ....................n_neighbors=73;, score=0.642 total time= 0.6s
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[CV 1/5] END ....................n_neighbors=74;, score=0.643 total time= 0.6s
[CV 2/5] END ....................n_neighbors=74;, score=0.652 total time= 0.4s
[CV 3/5] END ....................n_neighbors=74;, score=0.642 total time= 0.4s
[CV 4/5] END ....................n_neighbors=74;, score=0.639 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=75;, score=0.641 total time= 0.4s
[CV 2/5] END ....................n_neighbors=75;, score=0.651 total time= 0.4s
[CV 3/5] END ....................n_neighbors=75;, score=0.643 total time= 0.4s
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[CV 4/5] END ....................n_neighbors=75;, score=0.637 total time= 0.5s
[CV 1/5] END ....................n_neighbors=76;, score=0.639 total time= 0.4s
[CV 2/5] END ....................n_neighbors=76;, score=0.651 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=77;, score=0.635 total time= 0.4s
[CV 2/5] END ....................n_neighbors=77;, score=0.652 total time= 0.4s
[CV 3/5] END ....................n_neighbors=77;, score=0.641 total time= 0.4s
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[CV 4/5] END ....................n_neighbors=77;, score=0.641 total time= 0.5s
[CV 1/5] END ....................n_neighbors=78;, score=0.639 total time= 0.4s
[CV 2/5] END ....................n_neighbors=78;, score=0.650 total time= 0.4s
[CV 3/5] END ....................n_neighbors=78;, score=0.642 total time= 0.4s
[CV 4/5] END ....................n_neighbors=78;, score=0.640 total time= 0.5s
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[CV 1/5] END ....................n_neighbors=79;, score=0.635 total time= 0.4s
[CV 2/5] END ....................n_neighbors=79;, score=0.648 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=80;, score=0.636 total time= 0.4s
[CV 2/5] END ....................n_neighbors=80;, score=0.646 total time= 0.4s
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[CV 4/5] END ....................n_neighbors=80;, score=0.644 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=81;, score=0.634 total time= 0.4s
[CV 4/5] END ....................n_neighbors=81;, score=0.643 total time= 0.4s
[CV 3/5] END ....................n_neighbors=81;, score=0.640 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=82;, score=0.636 total time= 0.4s
[CV 2/5] END ....................n_neighbors=82;, score=0.647 total time= 0.4s
[CV 3/5] END ....................n_neighbors=82;, score=0.641 total time= 0.5s
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[CV 1/5] END ....................n_neighbors=83;, score=0.635 total time= 0.4s
[CV 3/5] END ....................n_neighbors=83;, score=0.639 total time= 0.4s
[CV 2/5] END ....................n_neighbors=83;, score=0.645 total time= 0.5s
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[CV 1/5] END ....................n_neighbors=84;, score=0.635 total time= 0.4s
[CV 2/5] END ....................n_neighbors=84;, score=0.645 total time= 0.5s
[CV 3/5] END ....................n_neighbors=84;, score=0.638 total time= 0.4s
[CV 4/5] END ....................n_neighbors=84;, score=0.645 total time= 0.4s
[CV 5/5] END ....................n_neighbors=84;, score=0.652 total time= 0.4s
[CV 1/5] END ....................n_neighbors=85;, score=0.631 total time= 0.5s
[CV 3/5] END ....................n_neighbors=85;, score=0.635 total time= 0.4s
[CV 2/5] END ....................n_neighbors=85;, score=0.642 total time= 0.4s
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[CV 2/5] END ....................n_neighbors=86;, score=0.642 total time= 0.4s
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[CV 3/5] END ....................n_neighbors=86;, score=0.634 total time= 0.4s
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[CV 2/5] END ....................n_neighbors=87;, score=0.639 total time= 0.5s
[CV 4/5] END ....................n_neighbors=87;, score=0.644 total time= 0.4s
[CV 3/5] END ....................n_neighbors=87;, score=0.633 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=88;, score=0.634 total time= 0.5s
[CV 2/5] END ....................n_neighbors=88;, score=0.640 total time= 0.4s
[CV 3/5] END ....................n_neighbors=88;, score=0.633 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=89;, score=0.633 total time= 0.4s
[CV 2/5] END ....................n_neighbors=89;, score=0.637 total time= 0.4s
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[CV 2/5] END ....................n_neighbors=90;, score=0.636 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=91;, score=0.633 total time= 0.5s
[CV 2/5] END ....................n_neighbors=91;, score=0.637 total time= 0.4s
[CV 3/5] END ....................n_neighbors=91;, score=0.631 total time= 0.4s
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[CV 4/5] END ....................n_neighbors=91;, score=0.642 total time= 0.5s
[CV 1/5] END ....................n_neighbors=92;, score=0.634 total time= 0.4s
[CV 2/5] END ....................n_neighbors=92;, score=0.640 total time= 0.4s
[CV 3/5] END ....................n_neighbors=92;, score=0.631 total time= 0.4s
[CV 4/5] END ....................n_neighbors=92;, score=0.642 total time= 0.5s
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[CV 1/5] END ....................n_neighbors=93;, score=0.633 total time= 0.4s
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[CV 4/5] END ....................n_neighbors=93;, score=0.639 total time= 0.4s
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[CV 1/5] END ....................n_neighbors=94;, score=0.632 total time= 0.4s
[CV 2/5] END ....................n_neighbors=94;, score=0.637 total time= 0.4s
[CV 3/5] END ....................n_neighbors=94;, score=0.634 total time= 0.5s
[CV 4/5] END ....................n_neighbors=94;, score=0.642 total time= 0.4s
[CV 5/5] END ....................n_neighbors=94;, score=0.647 total time= 0.4s
[CV 1/5] END ....................n_neighbors=95;, score=0.630 total time= 0.4s
[CV 3/5] END ....................n_neighbors=95;, score=0.635 total time= 0.4s
[CV 2/5] END ....................n_neighbors=95;, score=0.636 total time= 0.5s
[CV 4/5] END ....................n_neighbors=95;, score=0.638 total time= 0.4s
[CV 5/5] END ....................n_neighbors=95;, score=0.647 total time= 0.4s
[CV 1/5] END ....................n_neighbors=96;, score=0.632 total time= 0.4s
[CV 2/5] END ....................n_neighbors=96;, score=0.637 total time= 0.5s
[CV 3/5] END ....................n_neighbors=96;, score=0.635 total time= 0.4s
[CV 4/5] END ....................n_neighbors=96;, score=0.641 total time= 0.4s
[CV 5/5] END ....................n_neighbors=96;, score=0.647 total time= 0.4s
[CV 1/5] END ....................n_neighbors=97;, score=0.629 total time= 0.5s
[CV 2/5] END ....................n_neighbors=97;, score=0.638 total time= 0.4s
[CV 3/5] END ....................n_neighbors=97;, score=0.632 total time= 0.4s
[CV 4/5] END ....................n_neighbors=97;, score=0.639 total time= 0.4s
[CV 1/5] END ....................n_neighbors=98;, score=0.630 total time= 0.4s
[CV 5/5] END ....................n_neighbors=97;, score=0.645 total time= 0.5s
[CV 2/5] END ....................n_neighbors=98;, score=0.640 total time= 0.4s
[CV 3/5] END ....................n_neighbors=98;, score=0.632 total time= 0.4s
[CV 4/5] END ....................n_neighbors=98;, score=0.638 total time= 0.4s
[CV 1/5] END ....................n_neighbors=99;, score=0.628 total time= 0.4s
[CV 5/5] END ....................n_neighbors=98;, score=0.648 total time= 0.6s
[CV 2/5] END ....................n_neighbors=99;, score=0.637 total time= 0.5s
[CV 3/5] END ....................n_neighbors=99;, score=0.635 total time= 0.5s
[CV 4/5] END ....................n_neighbors=99;, score=0.635 total time= 0.4s
[CV 5/5] END ....................n_neighbors=99;, score=0.646 total time= 0.5s
Best parameters: {'n_neighbors': 1}
Precision: 0.6211829998263433
Recall: 0.5165562913907294
F1 Score: 0.5640589002108095
Weighted accuracy: 0.586105798963816
--------------------
Classification Report:
precision recall f1-score support
0 0.56 0.66 0.61 1413.6493732849112
1 0.62 0.52 0.56 1521.4751420454538
accuracy 0.59 2935.1245153303653
macro avg 0.59 0.59 0.59 2935.1245153303653
weighted avg 0.59 0.59 0.58 2935.1245153303653
Confusion Matrix:
[[934.3659423 479.28343099]
[735.54758523 785.92755682]]
--------------------
from sklearn.neighbors import KNeighborsClassifier
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
X_res, y_res = smote_sample(X_tr, y_tr)
tr_acc = []
te_acc = []
knn_best_model = None
def do_kNN(k: int):
global tr_acc
global te_acc
global knn_best_model
knn = KNeighborsClassifier(k)
knn.fit(X_res, y_res)
y_te_pred = knn.predict(X_te)
training_accuracy = knn.score(X_res, y_res)
validation_accuracy = weight_accuracy(y_te_pred, y_te, w_te)
if not te_acc or validation_accuracy > max(te_acc):
knn_best_model = knn
tr_acc.append(training_accuracy)
te_acc.append(validation_accuracy)
k_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
for k in k_values:
print(k)
do_kNN(k)
# Create a plot
plt.plot(k_values, tr_acc, marker='o', linestyle='-', label='Training Accuracy')
plt.plot(k_values, te_acc, marker='o', linestyle='-', label='Testing Accuracy')
# Add labels and title
plt.xlabel('k (number of neighbors)')
plt.ylabel('Error')
plt.title('Training and Testing Performance over k')
# Add a legend
plt.legend()
# Show the plot
plt.show()
1 2 3 4 5 6 7 8 9 10
import joblib
joblib.dump(knn_best_model, 'models/knn_best_model.joblib')
['knn_best_model.joblib']
from sklearn.neural_network import MLPClassifier
mlp_model = MLPClassifier()
mlp_param_grid = {
'hidden_layer_sizes': [(64,), (64, 64), (128,), (128, 128), (256, 256), (256, 256)],
'activation': ['relu'],
'alpha': [0.001, 0.01],
'max_iter': [100, 200, 300],
}
mlp_grid_search = do_grid_search_full_task(mlp_model, mlp_param_grid, n_jobs=7)
Fitting 5 folds for each of 36 candidates, totalling 180 fits [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=100;, score=0.633 total time= 0.7s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=100;, score=0.792 total time= 0.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=100;, score=0.716 total time= 0.9s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=200;, score=0.607 total time= 1.3s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=200;, score=0.642 total time= 1.4s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=200;, score=0.644 total time= 0.8s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=100;, score=0.795 total time= 1.8s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=100;, score=0.778 total time= 1.9s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=200;, score=0.720 total time= 1.3s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=200;, score=0.610 total time= 1.3s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=300;, score=0.761 total time= 0.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=300;, score=0.638 total time= 0.7s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=300;, score=0.742 total time= 1.3s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=300;, score=0.688 total time= 1.6s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64,), max_iter=300;, score=0.706 total time= 1.7s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.538 total time= 1.3s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.778 total time= 2.0s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.743 total time= 3.5s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.689 total time= 4.6s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.593 total time= 3.5s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.650 total time= 4.7s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.714 total time= 5.7s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.757 total time= 5.4s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.723 total time= 6.9s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.709 total time= 3.2s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.598 total time= 2.1s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.739 total time= 3.0s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=100;, score=0.634 total time= 2.3s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.616 total time= 5.8s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.618 total time= 3.5s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.760 total time= 3.5s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=100;, score=0.677 total time= 2.7s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=100;, score=0.747 total time= 2.4s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=100;, score=0.694 total time= 1.3s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=100;, score=0.775 total time= 2.0s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=200;, score=0.567 total time= 1.3s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=200;, score=0.766 total time= 1.7s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=300;, score=0.698 total time= 0.7s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=300;, score=0.651 total time= 1.2s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=200;, score=0.616 total time= 1.7s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=200;, score=0.645 total time= 2.1s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=200;, score=0.796 total time= 2.0s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=300;, score=0.557 total time= 1.3s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=300;, score=0.658 total time= 2.2s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128,), max_iter=300;, score=0.632 total time= 1.7s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.738 total time= 3.5s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.785 total time= 9.4s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.710 total time= 10.5s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.580 total time= 12.0s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.625 total time= 12.2s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.781 total time= 11.7s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.713 total time= 16.9s
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. warnings.warn(
[CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.716 total time= 19.3s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.731 total time= 11.9s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.590 total time= 5.4s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.577 total time= 10.8s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.674 total time= 8.3s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.767 total time= 16.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.785 total time= 18.4s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.753 total time= 20.4s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.624 total time= 24.5s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.589 total time= 18.5s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.780 total time= 42.5s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.645 total time= 34.1s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.698 total time= 44.7s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.672 total time= 51.7s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.631 total time= 44.8s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.653 total time= 30.7s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.655 total time= 43.8s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.772 total time= 37.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.768 total time= 49.7s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.596 total time= 43.7s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.767 total time= 30.3s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.695 total time= 31.5s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.666 total time= 50.1s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.555 total time= 26.8s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.683 total time= 58.9s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.740 total time= 34.6s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.585 total time= 23.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.695 total time= 41.4s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.780 total time= 36.8s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.611 total time= 59.1s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.725 total time= 52.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.657 total time= 38.6s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=100;, score=0.754 total time= 1.1s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=100;, score=0.638 total time= 2.0s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=100;, score=0.772 total time= 0.8s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=100;, score=0.686 total time= 1.7s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=100;, score=0.571 total time= 0.6s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=200;, score=0.770 total time= 1.1s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.781 total time= 1.2min [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=200;, score=0.788 total time= 1.4s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=200;, score=0.668 total time= 0.9s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=200;, score=0.738 total time= 1.0s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=200;, score=0.688 total time= 1.8s [CV 1/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.670 total time= 44.3s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=300;, score=0.765 total time= 1.2s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=300;, score=0.749 total time= 1.2s [CV 3/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.781 total time= 31.7s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=300;, score=0.749 total time= 1.1s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=300;, score=0.721 total time= 0.8s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64,), max_iter=300;, score=0.597 total time= 1.7s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.631 total time= 2.5s [CV 4/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.594 total time= 25.5s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.569 total time= 1.9s [CV 5/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.631 total time= 26.4s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.563 total time= 1.4s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.744 total time= 2.5s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.749 total time= 4.7s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.640 total time= 2.4s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=100;, score=0.751 total time= 6.2s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.538 total time= 3.0s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.620 total time= 3.9s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.766 total time= 1.8s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.522 total time= 2.3s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.738 total time= 4.1s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=100;, score=0.600 total time= 1.7s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=100;, score=0.724 total time= 1.2s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=200;, score=0.631 total time= 4.9s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=100;, score=0.699 total time= 1.1s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.785 total time= 4.3s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=100;, score=0.715 total time= 1.7s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=200;, score=0.641 total time= 1.5s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(64, 64), max_iter=300;, score=0.736 total time= 4.2s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=100;, score=0.686 total time= 2.4s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=200;, score=0.740 total time= 1.1s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=200;, score=0.711 total time= 1.1s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=200;, score=0.746 total time= 2.3s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=200;, score=0.709 total time= 1.6s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=300;, score=0.553 total time= 1.1s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=300;, score=0.779 total time= 1.0s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=300;, score=0.767 total time= 2.3s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=300;, score=0.742 total time= 2.0s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128,), max_iter=300;, score=0.736 total time= 2.4s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.737 total time= 4.6s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.565 total time= 8.5s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.599 total time= 9.2s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.574 total time= 11.1s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.645 total time= 10.9s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=100;, score=0.645 total time= 14.7s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.527 total time= 7.6s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.736 total time= 10.1s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.785 total time= 14.8s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=200;, score=0.660 total time= 18.8s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.541 total time= 6.9s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.542 total time= 16.0s [CV 2/5] END activation=relu, alpha=0.001, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.806 total time= 1.4min [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.784 total time= 16.3s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.576 total time= 7.2s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(128, 128), max_iter=300;, score=0.709 total time= 18.8s [CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.718 total time= 20.7s [CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.588 total time= 31.9s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.577 total time= 29.2s [CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.775 total time= 44.4s [CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.655 total time= 58.2s [CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.575 total time= 1.1min
/Users/brianlin/anaconda3/envs/184a_stroke_prediction/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet. warnings.warn(
[CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.704 total time= 1.1min
[CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.598 total time= 35.9s
[CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.608 total time= 44.1s
[CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.575 total time= 47.2s
[CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.760 total time= 1.2min
[CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.606 total time= 44.9s
[CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.553 total time= 45.2s
[CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.584 total time= 41.4s
[CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.760 total time= 1.1min
[CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.655 total time= 27.5s
[CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.797 total time= 1.2min
[CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.574 total time= 21.8s
[CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.622 total time= 47.3s
[CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.548 total time= 20.5s
[CV 1/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.741 total time= 16.4s
[CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.798 total time= 57.1s
[CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.602 total time= 24.1s
[CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=100;, score=0.743 total time= 59.6s
[CV 3/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.610 total time= 11.6s
[CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.540 total time= 31.3s
[CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=200;, score=0.662 total time= 46.0s
[CV 2/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.759 total time= 28.7s
[CV 4/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.682 total time= 35.2s
[CV 5/5] END activation=relu, alpha=0.01, hidden_layer_sizes=(256, 256), max_iter=300;, score=0.782 total time= 33.1s
Best parameters: {'activation': 'relu', 'alpha': 0.001, 'hidden_layer_sizes': (128, 128), 'max_iter': 200}
Precision: 0.8276214920017139
Recall: 0.6026490066225181
F1 Score: 0.6974418761961997
Weighted accuracy: 0.7289598865052401
--------------------
Classification Report:
precision recall f1-score support
0 0.67 0.86 0.75 1413.6493732849112
1 0.83 0.60 0.70 1521.4751420454538
accuracy 0.73 2935.1245153303653
macro avg 0.75 0.73 0.73 2935.1245153303653
weighted avg 0.75 0.73 0.72 2935.1245153303653
Confusion Matrix:
[[1222.67255062 190.97682267]
[ 604.55965909 916.91548295]]
--------------------
from sklearn.neural_network import MLPClassifier
X_tr, X_te, y_tr, y_te, w_tr, w_te = train_test_weight_data()
X_res, y_res = smote_sample(X_tr, y_tr)
tr_acc_grid = []
te_acc_grid = []
highest_te_acc = -1
mlp_best_model = None
def do_MLP(hs, hl):
mlp_shape = np.array([hs] * hl)
print(mlp_shape)
mlp = MLPClassifier(hidden_layer_sizes=mlp_shape, activation='relu', alpha=0.001, max_iter=200)
mlp.fit(X_res, y_res)
tr_acc = mlp.score(X_res, y_res)
y_te_pred = mlp.predict(X_te)
te_acc = weight_accuracy(y_te_pred, y_te, w_te)
eval_model(y_te_pred, y_te, w_te)
return tr_acc, te_acc, mlp
for hl in range(1, 4):
tr_lst = []
te_lst = []
for hs in [64, 128, 256, 512]:
tr_acc, te_acc, mlp = do_MLP(hs, hl)
if te_acc > highest_te_acc:
highest_te_acc = te_acc
mlp_best_model = mlp
tr_lst.append(tr_acc)
te_lst.append(te_acc)
tr_acc_grid.append(tr_lst)
te_acc_grid.append(te_lst)
[64]
Precision: 0.8410999238936518
Recall: 0.3642384105960267
F1 Score: 0.5083400911847232
Weighted accuracy: 0.6347716316990415
--------------------
Classification Report:
precision recall f1-score support
0 0.58 0.93 0.71 1413.6493732849112
1 0.84 0.36 0.51 1521.4751420454538
accuracy 0.63 2935.1245153303653
macro avg 0.71 0.65 0.61 2935.1245153303653
weighted avg 0.71 0.63 0.61 2935.1245153303653
Confusion Matrix:
[[1308.95409034 104.69528295]
[ 967.29545455 554.1796875 ]]
[128]
Precision: 0.7980179386682487
Recall: 0.7814569536423853
F1 Score: 0.7896506241911433
Weighted accuracy: 0.78418599132649
--------------------
Classification Report:
precision recall f1-score support
0 0.77 0.79 0.78 1413.6493732849112
1 0.80 0.78 0.79 1521.4751420454538
accuracy 0.78 2935.1245153303653
macro avg 0.78 0.78 0.78 2935.1245153303653
weighted avg 0.78 0.78 0.78 2935.1245153303653
Confusion Matrix:
[[1112.71619818 300.93317511]
[ 332.5078125 1188.96732955]]
[256]
Precision: 0.7858684669002297
Recall: 0.7880794701986767
F1 Score: 0.7869724155960579
Weighted accuracy: 0.778835878878141
--------------------
Classification Report:
precision recall f1-score support
0 0.77 0.77 0.77 1413.6493732849112
1 0.79 0.79 0.79 1521.4751420454538
accuracy 0.78 2935.1245153303653
macro avg 0.78 0.78 0.78 2935.1245153303653
weighted avg 0.78 0.78 0.78 2935.1245153303653
Confusion Matrix:
[[1086.93695765 326.71241563]
[ 322.43181818 1199.04332386]]
[512]
Precision: 0.8645719781981385
Recall: 0.29801324503311266
F1 Score: 0.44324303395394954
Weighted accuracy: 0.6119143055229539
--------------------
Classification Report:
precision recall f1-score support
0 0.56 0.95 0.70 1413.6493732849112
1 0.86 0.30 0.44 1521.4751420454536
accuracy 0.61 2935.124515330365
macro avg 0.71 0.62 0.57 2935.124515330365
weighted avg 0.72 0.61 0.57 2935.124515330365
Confusion Matrix:
[[1342.6249351 71.02443818]
[1068.05539773 453.41974432]]
[64 64]
Precision: 0.5378508369755198
Recall: 0.9867549668874173
F1 Score: 0.6962153541399759
Weighted accuracy: 0.5536249920752884
--------------------
Classification Report:
precision recall f1-score support
0 0.86 0.09 0.16 1413.6493732849112
1 0.54 0.99 0.70 1521.4751420454538
accuracy 0.55 2935.1245153303653
macro avg 0.70 0.54 0.43 2935.1245153303653
weighted avg 0.69 0.55 0.44 2935.1245153303653
Confusion Matrix:
[[ 123.63513313 1290.01424015]
[ 20.15198864 1501.32315341]]
[128 128]
Precision: 0.8259392189553884
Recall: 0.5496688741721867
F1 Score: 0.6600616598375302
Weighted accuracy: 0.7065155371716489
--------------------
Classification Report:
precision recall f1-score support
0 0.64 0.88 0.74 1413.6493732849112
1 0.83 0.55 0.66 1521.4751420454538
accuracy 0.71 2935.1245153303653
macro avg 0.73 0.71 0.70 2935.1245153303653
weighted avg 0.74 0.71 0.70 2935.1245153303653
Confusion Matrix:
[[1237.40354521 176.24582808]
[ 685.16761364 836.30752841]]
[256 256]
Precision: 0.8430071402073295
Recall: 0.3973509933774838
F1 Score: 0.5401177539225441
Weighted accuracy: 0.6492474630058952
--------------------
Classification Report:
precision recall f1-score support
0 0.59 0.92 0.72 1413.6493732849112
1 0.84 0.40 0.54 1521.4751420454538
accuracy 0.65 2935.1245153303653
macro avg 0.71 0.66 0.63 2935.1245153303653
weighted avg 0.72 0.65 0.63 2935.1245153303653
Confusion Matrix:
[[1301.06248609 112.58688719]
[ 916.91548295 604.55965909]]
[512 512]
Precision: 0.8330758869542688
Recall: 0.5298013245033124
F1 Score: 0.6476954851246048
Weighted accuracy: 0.7012356969850312
--------------------
Classification Report:
precision recall f1-score support
0 0.64 0.89 0.74 1413.6493732849112
1 0.83 0.53 0.65 1521.4751420454538
accuracy 0.70 2935.1245153303653
macro avg 0.73 0.71 0.69 2935.1245153303653
weighted avg 0.74 0.70 0.69 2935.1245153303653
Confusion Matrix:
[[1252.13453979 161.51483349]
[ 715.39559659 806.07954545]]
[64 64 64]
Precision: 0.7761721297839084
Recall: 0.7218543046357632
F1 Score: 0.7480284461600619
Weighted accuracy: 0.7479125191816841
--------------------
Classification Report:
precision recall f1-score support
0 0.72 0.78 0.75 1413.6493732849112
1 0.78 0.72 0.75 1521.4751420454538
accuracy 0.75 2935.1245153303653
macro avg 0.75 0.75 0.75 2935.1245153303653
weighted avg 0.75 0.75 0.75 2935.1245153303653
Confusion Matrix:
[[1096.93298969 316.71638359]
[ 423.19176136 1098.28338068]]
[128 128 128]
Precision: 0.7276963209498968
Recall: 0.03973509933774837
F1 Score: 0.07535549063086405
Weighted accuracy: 0.4945216915959444
--------------------
Classification Report:
precision recall f1-score support
0 0.49 0.98 0.65 1413.6493732849112
1 0.73 0.04 0.08 1521.4751420454538
accuracy 0.49 2935.1245153303653
macro avg 0.61 0.51 0.36 2935.1245153303653
weighted avg 0.61 0.49 0.35 2935.1245153303653
Confusion Matrix:
[[1391.02677446 22.62259883]
[1461.01917614 60.45596591]]
[256 256 256]
Precision: 0.3157685191690669
Recall: 0.013245033112582788
F1 Score: 0.025423660899689875
Weighted accuracy: 0.473620276704689
--------------------
Classification Report:
precision recall f1-score support
0 0.48 0.97 0.64 1413.6493732849112
1 0.32 0.01 0.03 1521.4751420454538
accuracy 0.47 2935.1245153303653
macro avg 0.40 0.49 0.33 2935.1245153303653
weighted avg 0.39 0.47 0.32 2935.1245153303653
Confusion Matrix:
[[1369.98249648 43.66687681]
[1501.32315341 20.15198864]]
[512 512 512]
Precision: 0.5179200941575226
Recall: 0.9933774834437087
F1 Score: 0.6808588426056237
Weighted accuracy: 0.5172654618708165
--------------------
Classification Report:
precision recall f1-score support
0 0.40 0.00 0.01 1413.6493732849112
1 0.52 0.99 0.68 1521.4751420454538
accuracy 0.52 2935.1245153303653
macro avg 0.46 0.50 0.35 2935.1245153303653
weighted avg 0.46 0.52 0.36 2935.1245153303653
Confusion Matrix:
[[ 6.83939034 1406.80998294]
[ 10.07599432 1511.39914773]]
import matplotlib.pyplot as plt
import seaborn as sns
np_tr_acc_grid = np.array(tr_acc_grid)
np_te_acc_grid = np.array(te_acc_grid)
ax = sns.heatmap(np_te_acc_grid, linewidth=0.5, annot=True, cmap='vlag', fmt='.4f', cbar_kws={'label': 'Accuracy Score'})
plt.title('Grid Search Results')
plt.xlabel('hidden_nodes')
plt.ylabel('hidden_layers')
custom_xticks_positions = [0.5, 1.5, 2.5, 3.5]
custom_xticklabels = ['64', '128', '256', '512']
plt.xticks(custom_xticks_positions, custom_xticklabels)
custom_yticks_positions = [0.5, 1.5, 2.5]
custom_yticklabels = ['1', '2', '3']
plt.yticks(custom_yticks_positions, custom_yticklabels)
print(np_tr_acc_grid)
print(np_te_acc_grid)
print(mlp_best_model.get_params)
[[0.65741941 0.79524824 0.79668396 0.62782512] [0.54390515 0.70975361 0.65987403 0.70276028] [0.78385513 0.53181734 0.51236569 0.50175991]] [[0.63477163 0.78418599 0.77883588 0.61191431] [0.55362499 0.70651554 0.64924746 0.7012357 ] [0.74791252 0.49452169 0.47362028 0.51726546]] <bound method BaseEstimator.get_params of MLPClassifier(alpha=0.001, hidden_layer_sizes=array([128]))>
import joblib
joblib.dump(mlp_best_model, 'models/mlp_best_model.joblib')
['mlp_best_model.joblib']